RSS Feed for the unit Knowledge technologies in context

Introduction

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Knowledge technologies embody formal models of how the world works. If well designed, these models can relieve people of mundane activities and free them up to concentrate on what they do best. At their best, knowledge technologies can detect patterns in information which are too complex for humans to detect, or which they do not have time to detect, and can deliver this information to the right people, at the right time, in the right form for interpretation. This unit looks at the core concepts of representation, interpretation, situated use in context and communities of practice to highlight how such tools are subsequently integrated into the cognitive, social and organisational flow of work.

You will see how new technologies can trigger changes in the ecology of work, which adapts to try to incorporate the technologies into work practice. In the worst case, no ecological niche can be found and the system is rejected or worked around. In the best case, the ecosystem works more efficiently because of mediating new activities technologically. Of course, there are many non-technological dimensions to understanding what it might mean to ‘manage knowledge’. However, it is fair to say that technology is a thread weaving throughout, and it appears now to be a permanent feature in knowledge management conferences and publications. Can ‘knowledge’ be managed as an objectified asset? And what does this mean in different contexts? In this unit you will explore answers to these questions.

This unit is an adapted extract from the Open University course Managing knowledge (B823).

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Learning outcomes

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

After studying this unit you should be able to demonstrate an understanding of the following issues, explaining in your own words, with appropriate examples:

the importance of representation, interpretation and formalisation in relation to ICT and managing knowledge;
the concept of a ‘community of practice’ in relation to ICT;
the main functions that ICT can play in helping to manage knowledge;
the potential, and problems, of ICT for managing explicit knowledge;
the potential, and problems, in the relationship between ICT and ‘the tacit dimension’.

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1 Knowledge technologies in context

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

There are many non-technological dimensions to understanding what it might mean to ‘manage knowledge’. However, technology is a thread weaving throughout, and seems now to be a fixture in knowledge management conferences and publications. ‘Knowledge’ can be managed as an objectified asset is a core idea in knowledge management. This unit will encourage you to question what this means in different contexts. ‘Context’ allows us to considere what value is added by viewing management and the firm through a ‘knowledge lens’, and in this unit you will encounter it again.

You may be wondering exactly what the relationship is between information, knowledge and technology. How do the tacit and explicit dimensions of knowing relate to what can be stored on a hard disk, represented in software, or even reasoned about by a computer system? This unit explores the fascinating interaction between humans and machines in the context of designing computational support for knowledge work.

Digital representations lie at the heart of information and communication technologies (ICT). Computers depend on digital input in order to acquire data about some aspect of the world. This ‘capture process’ can range from automated logging of raw data, to asking people to manually enter and classify information, to classifying information automatically. To enable useful searching and reuse of the resulting repository, the contents must be segmented and indexed so that relevant parts can be retrieved. Whoever or whatever performs this task, one or more classification schemes must be used which reflect a view of what is important and meaningful. The process of developing a classification or structuring scheme will be referred to as formalisation, which, as we shall see, is a critical process for humans, both cognitively and socially.

Human understanding and expertise, in contrast, is always evolving and is embedded in social interaction within communities. Meaning and significance are context-dependent properties and are clothed in multiple modalities, not just those which can be verbalised or codified as text or digits. We solve problems in opportunistic ways, rarely following idealised, predetermined procedures. We interact and communicate by non-verbal as well as verbal means, negotiating social conventions which are rarely articulated. Expert performance draws on knowledge that is hard to express and structure explicitly. Information and knowledge have social and political dimensions which are never recorded, but which are powerful determinants of organisational behaviour.

How can such different partners as computers and humans build a harmonious marriage?

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1.1 ‘Technology’?

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

In knowledge management literature the term ‘technology’ is assumed to mean digital media and networks: software and hardware that comprise today's ICTs. However, it is important to remember that pens and paper are forms of technology, along with whiteboards, sticky notes, and the other non-digital media that make up the infrastructure of our daily lives at work. These are not about to disappear: paper is robust and portable, text on paper is easily read and annotated, and most organisational, legal and financial systems still operate around signatures on paper! There is growing acceptance of ‘digital signature’ technology, but, culturally, institutions are still ‘papyrocentric’ in many respects.

Figure 1

Long description

It is worth noting that technologies of symbolic representation were originally the great breakthrough of literacy in society: writing, assisted by the technology of printing, enabled ideas to exist separately from people, and to be communicated across time and space for others to share and evaluate. People from primary oral cultures, who had no method for recording speech, had to develop ways to preserve knowledge and history through highly structured narrative techniques such as recitation, poetry and song. Interestingly, an important theme that is emerging in knowledge management research is the role of informal social contact for knowledge sharing, and the role of stories in organisations (see Section 4.2).

We should not forget that language is an aural technology that has taken each of us years to learn, and which we then learn to adapt to different cultural contexts. In this unit we will be discussing technologies for communities of practice, a defining feature of which is the way in which they talk. Although we do not go into the issue of language in any depth, this unit's core concepts of knowledge, codification and representation are skirting a vast research literature on how language influences knowledge and meaning.

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1.2 Pressing questions

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

In the late 1990s, when this unit was first prepared, if you surveyed the field of knowledge management technology you were assailed by technology vendors offering Knowledge Management Solutions. As we write in 2005 , an internet search on ‘knowledge management ICT’ will still return thousands of hits, but the ‘knowledge’ buzzword has faded in potency, the hype bandwagon has trundled on, and vendors now market the same products under business process banners which reflect greater realism about their scope: for instance, document management, workflow management, shared workspaces, virtual meetings, text mining, data integration, information visualisation.

Where does this leave us in terms of ‘knowledge technologies’? You may still be asking, or perhaps are being asked by others, how relevant these technologies are to your needs. For instance, you may have the following concerns:

Is my organisation really about to fall behind the competition and become a dinosaur of a rapidly passing era if it does not ‘get wired’ fast?
I accept that there is a difference between information and knowledge, but what implications does this have for all our current information technologies? Do we now have to ‘upgrade’ to knowledge technologies?
Show me some case studies where people have successfully introduced knowledge management technologies.
I'm already overloaded with information. I don't want to know about any new systems if they're going to aggravate this.
I've heard that knowledge management systems are simply the best way to generate a vast repository of out-of-date knowledge.

After working through this unit, you will have encountered, and reflected critically on, some responses to these concerns.

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1.3 Scope of this unit

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

ICT technical developments are announced on almost a monthly basis, so this unit cannot provide an up-to-the-minute snapshot of knowledge management technologies. While we describe many examples of relevant technologies, it is important not to let these particular examples constrain how you think about the possibilities; they are simply examples of commercial products and point to emerging technologies in research laboratories.

Our emphasis, therefore, is on providing conceptual frameworks that will outlive any short-term technical innovations. These draw attention to persistent human and technology-related issues – invariably interactions between the two – with which any knowledge management initiative must grapple. Our intention is that this will provide you with robust ways to reflect on the scope and potential of new technologies in relation to organisational knowledge processes and objectives.

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1.4 Aims

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

The aims of this unit are:

to develop an understanding of the relationships between information, interpretation, knowledge and computer-based representations
to summarise the range of different technologies that are available and on the horizon, and how they relate to different kinds of knowledge processes
to provide frameworks for thinking about technologies for managing knowledge, and for evaluating the claims made by technology vendors and researchers.

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2.1 Representation, interpretation and communities of practice

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Let us start with a thought experiment.

Activity 2.1

Where is the music?
The music is in the musical notation.
No, the music is in the mind of the composer.
No, the music is in the performance.
No, the music is in the hearing.
For ‘music’ read ‘knowledge’.

‘What is knowledge?’ is obviously a weighty question. In thinking about knowledge specifically in relation to ICT, we will introduce a key concept: representation. Representation is the way in which information is made manipulable and shareable. Representations may be designed to be written and read by people or computers, providing the basis for human-human, computer-computer and human-computer interaction. Their design involves formulating a language to describe some aspect of the world. To create a representation (computer-based or not), we must codify information; that is, translate it into the vocabulary and grammar of the particular language. Codification is therefore critical, as we will see in Section 2.2.

A further concept for clarifying distinctions between information technology and knowledge technology is interpretation. Representations embody information, but they are useless – indeed meaningless – unless someone or something (another computer) interprets them. We define interpretation here with an intentionally practical orientation: the process of assessing information (perceived via one or more representational medium) with respect to a goal (for example, to solve a problem; to judge someone's character; to gauge the tone of a meeting). Once information has been interpreted, you have knowledge for action, even if it is to decide that the information is irrelevant. A consequence of this view of knowledge is that information may be interpreted in many different ways – its significance depends on the reader. As an expert, you may be able to glance at a spreadsheet and immediately spot a statistical trend that a junior member of staff has missed; or you may glean implicit messages from reading a memo which a less experienced colleague would miss.

An implication of this view is that, while information technologies deliver data structured using different representations, knowledge technologies – if we can justify this term – will be distinguished by their support for interpreting those representations: for instance, by making it easier to access and understand context, or at least, by linking to people who can help supply missing context. A further implication is that, if knowledge is the contextualised interpretation of information, a computer can be said to ‘know’ something in a limited sense if it has the ability to reason about information; that is, if it can interpret information with a notion of ‘context’ and act appropriately on it in some way. We describe such systems in ‘Example: an “intelligent” email system’ in Section 4.3.

If context and interpretation are key to ascribing meaning to information, a radical constructivist implication is that, since each of us encounters the world through our own particular lens, in principle there is no such thing as ‘codified knowledge’ in an artefact, whether digital or paper.

Activity 2.2

A particular view of the relationship between knowledge and technologies has been set out. Do you agree with the idea that it is not possible for ‘knowledge’ to exist in a form that can be stored and embodied in objects and documents, or do you think this is rather an extreme view? Is it in fact the case that manufactured objects do contain the design knowledge that went into them, and that patents and books do contain knowledge? Communities of practice, for example, are a foundation for effective teamwork and the sharing of tacit knowledge.

Discussion

If you answered ‘no’ to the first question and ‘yes’ to the second, does this mean that the knowledge embodied in an object is the same regardless of who interprets it? After all, we do not normally expect an object to change its properties unless someone modifies it. You may find it helpful to think about documents or objects in your own work context. Perhaps some objects are more ambiguous (that is, more likely to change their meaning) than others? But then, surely it depends on the people who are interpreting them?

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2.2 Representation, interpretation and communities of practice continued

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

The preceding discussion brings us to a critical concept introduced earlier: the community of practice (Lave and Wenger, 1991; Wenger, 1998; Bowker and Star, 1999). Wenger emphasises that such communities are not the preserve of what are commonly conceived as knowledge workers. Wenger's central example is of a department of staff processing medical insurance claims, somewhat in contrast to the autonomous knowledge workers defined by Peter Drucker. In fact, as the term reflects, practice is the central concept. Consider the following introductory passage, which highlights for this unit some of the physical and digital technologies that contribute to the idea of practice:

The concept of practice connotes doing, but not just doing in and of itself. It is doing in a historical and social context that gives structure and meaning to what we do. In this sense, practice is always social practice.
Such a concept of practice includes both the explicit and the tacit. It includes what is said and what is left unsaid; what is represented and what is assumed. It includes language, tools, documents, images, symbols, well-defined roles, specified criteria, codified procedures, regulations, and contracts that various practices make explicit for a variety of purposes. But it also includes all the implicit relations, tacit conventions, subtle cues, untold rules of thumb, recognisable intuitions, specific perceptions, well-tuned sensitivities, embodied understandings, underlying assumptions, and shared world views. Most of these may never be articulated, yet they are unmistakable signs of membership in communities of practice and are crucial to the success of their enterprise.

(Wenger, 1998, p. 47, emphasis added)

‘Practice’, then, is the stuff of the familiar workplace. Practice is what distinguishes the insider from the outsider. Members of a community of practice can be thought of as possessing a particular literacy_: they know how to ‘read and write’ different kinds of artefacts, and how to engage in different ‘language games’ appropriately. Boland and Tenkasi (1995) talk of ‘interpretative strategies’ – ways of reading and writing, listening and speaking. When groups attempt to communicate without this common ground, we often witness breakdowns in interpretation. The importance of interpretation helps us understand a paradoxical implication of the community of practice perspective: a community of practice in one company (for example, biochemists) may find it easier to communicate with similar communities of practice in other organisations than with their own marketing department or electrical engineers.

A focus on communities of practice raises interesting questions when designing knowledge technologies (Blackler, 1995; Boland and Tenkasi, 1995). Communities of practice shape when, how and why knowledge is acquired, classified, shared, validated, transformed and stored. In the context of designing ‘knowledge technologies’, it should be clear that one ignores relevant communities of practice at one's peril. The challenge is to negotiate between different communities of practice, who will have different conceptions of ‘the problem’, and varying agendas and ways of thinking and talking about work. It follows from this view that the boundaries between communities of practice should not be conceived of simply as undesirable walls to be dismantled (perhaps using technology to span time, space and organisational hierarchies). It is these boundaries that communities of practice construct for themselves that enables them to evolve and sustain their shared practice. We return to the issue of boundaries later.

Finally, we need to attend to a concept which is implicit in the importance we are placing on knowledge arising from the interpretation of information within communities of practice: situatedness. This term is now being prefixed to a variety of concepts, for instance situated learning, situated knowledge, situated cognition and situated action. 'Situatedness’ refers to the view that the context in which knowledge is developed and deployed is fundamental. A growing body of research into cognition in everyday activity argues against the value of abstracting conceptual knowledge away from the situations in which it is learned and applied. Instead, it is argued that ‘knowledge is a product of the activity, context and culture in which it is developed and used’ (Brown et al., 1989, p. 32).

A useful overview of research into situated knowledge, and a proposal as to how it relates to different organisational structures, can be found in Blackler (1995). For those interested in exploring further the philosophical underpinnings to information technology design, a useful resource is the book by Coyne (1995).

Activity 2.3

The preceding discussion may have raised some questions in your mind about the credibility of ‘situated practice’ perspectives. After all, abstract representations are the bedrock of the scientific method and engineering. We learn about and model the world by making generalisations. Or, if you are sympathetic to the view that meaning derives from situated practice, what role does this leave for computer-mediated abstractions in knowledge technologies? If the most valuable knowledge is situated in the context of ongoing work and problem solving within a specific community of practice, to what extent is it meaningful to represent knowledge as hierarchies or networks of abstracted concepts for reuse across a large organisation, comprising multiple communities of practice?

Discussion

Proponents of situated knowledge do not reject abstractions out of hand, but often question their status, focusing on the way in which they are constructed, the actual ways in which they are used (that is, the practices) and the limitations of abstractions as descriptions of a complex world. Analyses of abstractions as varied as scientific theories and paradigms, the ‘professionalisation’ of jobs for auditing, medical classifications, and software design methodologies, have documented the ways in which they reflect idealisations of reality, and the fact they are often ‘worked around’ by those who either claim, or are required, to follow them. They emphasise the importance of the culture in which the abstractions are devised, which often ignores other perspectives. In the context of managing knowledge, therefore, we must attend to the ways in which ICT is used in situated ways by communities of practice, and be alert to the potential clash between the world view embodied in a software system, and a community's world view and work practices. Further light is shed on this in Section 2.2, which looks more closely at the dynamics of abstraction and decontextualisation.

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2.3 Codification and formalisation

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Much of the knowledge management literature argues the importance of making tacit knowledge explicit, and then codified. For instance, an explicit goal when auditing intellectual capital is to identify human capital as one of the key assets that give an organisation its true value. Some organisations are realising that a large quantity of their ‘assets’ leave the office for home each evening, perhaps never to return, and as a consequence want to capture these in a less vulnerable form. This means codifying them in some way. However, codification is not restricted to the field of intellectual capital; it pervades, indeed underpins, all attempts to systematise processes and records.

What does the codification of knowledge entail? An answer to this must be grounded in an understanding of how knowledge gets transformed from the mind of an individual to reside eventually on a computer. You may have considered tacit-explicit knowledge. In order to understand how technologies fit in, we must investigate the tacit-explicit continuum in more detail, since it lies at the heart of the digital codification process. What happens to knowledge as it is codified? What is gained and what is lost?

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2.3.1 From tacit pre-understanding to symbolic representation

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

This section reflects many of the critiques that have been made of efforts to apply technology to knowledge work without taking seriously the differences between human and artificial knowledge representations. Stahl (1993a,b) has presented an informative analysis of the transformation of knowledge from tacit to explicit to formally codified representations in computer-interpretable form, emphasising the centrality of interpretation situated in the workplace (Figure 2).

Stahl also seeks to clarify how individual knowledge, through becoming shared knowledge, and subsequently codified in computer-interpretable form, moves from hermeneutic presence to symbolic representation (Figure 3). ‘Hermeneutic presence’ is a term taken from the philosopher Heidegger, and refers to tacit knowledge that underpins individual and collective understanding; it shapes our perception of the world, and we cannot step completely outside this perception. Although we can rationally critique what we have previously taken for granted, this then changes the tacit pre-understanding we bring to future situations. In contrast, ‘symbolic representation’ enables us to treat information and ideas as separate from ourselves – once codified, they can be manipulated and analysed (hence the possibility for self-reflection and the learning of abstract concepts).

(Source: based on Stahl, 1993a)

Figure 2 Transformation of knowledge from tacit ‘pre-understanding’ to explicit, computer-based models

Long description

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2.4 Codification and formalisation continued

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

An important point is that the process of ‘objectifying’ knowledge brings with it a gradual change in the knowledge represented, because content and form are inextricably linked. McLuhan's famous quotation ‘the medium is the message’ highlights this phenomenon, but overstates the case a little. We can say that the medium shapes the message, as follows:

(Source: based on Stahl, 1993a)

Figure 3 ‘Hermeneutic presence’ and symbolic representations

Long description

As we move from tacit, individual, pre-understanding to shared, formal, computer-based representation, we express our thoughts in an increasingly structured way, providing the computer with greater access to the content of the information. The intellectual effort required to transform knowledge representations from one state to another can lead to new insights, since the particular representation used forces us to make certain information explicit that was previously implicit. Typically, ‘information chunks’ have to be broken down into smaller units of particular classes, given names, classified and structured. Having to reason about these can clarify our thinking.
However, as we move from tacit towards finer-grained symbolic representations, we strip away details of the context(s) in which that knowledge was displayed and/or has meaning. It is usually difficult, and often impossible, to reverse the direction and recover tacit pre-understanding from symbolic representations. The ‘knowledge processes’ in Figure 2 should be understood as interpretative acts; that is, in a situation, from a perspective, for a purpose. The transformations bring about ontological changes (see Box 2.1) that unavoidably distort knowledge and ‘alienate’ it from the person possessing it in particular ways, effecting a gradual shift in definition of knowledge and expertise from an ability to a symbolically encoded fact. This critical standpoint is not intended to be ‘anti-technology’; rather, it is a principled basis on which to understand how technologies come to embody and perpetuate world views and associated value systems.
Different ways of codifying knowledge give us different languages in which to describe the world. If we take seriously the argument that the language we use to talk about the world constrains and shapes our understanding, then we can even talk about the possibility of an ‘ontological shift’ taking place – the set of distinctions we regard as important to make when describing the world changes, depending on the representation we are using and the reasons for using it.

Box 2.1 Ontology

In philosophy, an ontology refers to ‘being’ or ‘the nature of being in the world’. The term ontology has been appropriated by artificial intelligence research to mean a ‘reusable terminological scheme’; that is, a scheme for providing a rigorous description of the concepts and attributes, and their interrelationships, that are deemed relevant to describe a particular aspect of the world. Its precision means that it can serve as a ‘technical dictionary’ to ensure a common point of reference in a complex area. An ontology is an abstract knowledge model which does not need software to exist. However, a strength is that it can also be implemented as software to build a knowledge-based system. We return to ontologies in Section 4.3.

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2.4.1 From Heidegger to knowledge technologies

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Because each transformation from one ‘knowledge state’ to another (Figure 2) is an act of interpretation, there is no such thing as objective knowledge representation, or indeed objective classification or codification of any sort (in software or any other medium): there is always a viewpoint. This leads to the view that information and communication systems cannot be thought of as neutral; in their formal structures and operations they embody the goals and perspectives of their developers.

Similarly, attempts to make tacit knowledge explicit run a number of unpredictable risks. Think of the fabled centipede that became paralysed when it tried to think about how it walked. We can perhaps relate a little more closely to the example of sociology students who report that learning how people use non-verbal cues to take turns in conversations has a debilitating impact on their own social skills! These examples point to the impact of asking people to reconceptualise their activities using abstract, descriptive ideas. They illustrate the codification process of moving from tacit to explicit knowledge. The analytical process of studying complex behaviour provides researchers with the vocabulary they need to discuss it, but this symbolic representation is qualitatively different from the tacit, embodied skills being described.

This subtle change in the quality of information is one of the reasons why it is so hard to build expert systems for problems that are not already very well understood, with well-defined boundaries for the nature and numbers of variables that can arise, and the methods that can be used to cope with them. The most successful expert systems ‘simply’ (they are still complex) manage large networks of interdependences between variables which are cognitively too complex for humans to track easily.

A more down-to-earth manifestation of the formalisation problem is that people often find it hard to fill in forms with predefined checkboxes and questions. The form serves the specific purpose of structuring information from the messy world into predefined, abstract categories. Mismatches arise when we do not think about our work according to those categories, when questions are asked at the wrong level of detail, or when we are not asked for information which we deem essential to a proper understanding of what we do. We end up ‘shoe-horning’ information about an embodied activity into decontextualised, symbolic representations ideal for computational analysis and recombination with other data sources. Naturally, we wonder about the value of the data and how our form-based answers will feed into subsequent decision making.

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2.5 Design implications

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

The difficulties just described have very practical implications when it comes to designing technologies. Consider the following quotations:

in selecting any representation we are in the very same act unavoidably making a set of decisions about how and what to see in the world …
a knowledge representation is a set of ontological commitments. It is unavoidably so because of the inevitable imperfections of representations. It is usefully so because judicious selection of commitments provides the opportunity to focus attention on aspects of the world we believe to be relevant.
… In telling us what and how to see, they allow us to cope with what would otherwise be untenable complexity and detail. Hence the ontological commitment made by a representation can be one of the most important contributions it offers.

(Davis et al., 1993)

Classification systems provide both a warrant and a tool for forgetting.
The classification system tells you what to forget and how to forget it.
The argument comes down to asking not only what gets coded in but what gets read out of a given scheme.

(Bowker and Star, 1999, pp. 277, 278, 281)

The first quotation is from a group of knowledge engineers, the following three from anthropologists studying the impact of ‘professionalisation’ and information technology in organisations. All draw attention to the ontological commitments that we make in choosing a representation: it acts as a filter on the inevitable messy complexity of the world we wish to describe. In the process of simplifying a problem in order to codify it systematically, whether for human or computer analysis, we may also be systematically filtering out critical, tacit, situated knowledge, simply because it is hard to systematise and formalise. It is important not to generalise before understanding the particular. The art of representation raises two fundamental questions:

What am I going to represent? Do we understand the world we are trying to describe in enough depth to know what detail can be safely ignored?
How will this representation scheme be used, by whom, with what training? How can we assist interpretation through training (that is, changing the people), and/or by the careful design of representations (that is, changing the computer)?

In the light of our discussion, in Box 2.2 we re-express our conception of how information, knowledge and representations interrelate with some key design criteria.

Box 2.2 Criteria for knowledge representation

Human knowledge begins as tacit, uncodified and situated understanding, and evolves through interaction with the world and symbolic representations, which are subject to continuous, active interpretation. How can computers support the process of making human knowledge more explicit – and hence shareable – without in the process:

freezing the knowledge in an inert state which cannot keep up with the changing world it claims to describe?
distorting the knowledge because the representations used to codify it are not rich enough to express important aspects of the world?
disrupting the work that people have to do because of the difficulty of encoding knowledge in computer-readable form?

Essentially, we see knowledge as arising from the interaction between people and information, mediated via representations. Since individuals can read different interpretations into the same representation, we cannot talk about stored knowledge whose meaning is fixed and unambiguous. Meaning is the understanding that emerges as the result of an interpretative process.

An approach focused on representations and situated interpretation within communities of practice leads us to questions rarely raised by a technocentric perspective:

What communities of practice need to be considered? These are the generators and consumers of knowledge within the organisation.
What representations will help bridge the boundaries between communities?
What expertise is required to interpret a given information source appropriately?
Who gets to design the representations which will be embedded in the system?

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3.1 A knowledge management technology framework

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

In the introduction to a book on knowledge management technologies, Borghoff and Pareschi (1998) described a framework for organisational memory that has been developed within Xerox to promote understanding of the roles and interplay between different technologies (Figure 4).

(Source: Borghoff and Pareschi, 1998, p. 5)

Figure 4 A framework describing the interplay of different technologies in constructing organisational memory

Long description

While the example technologies and techniques described in the different boxes will evolve, the broad differentiation of roles is a useful one to bear in mind. Borghoff and Pareschi map their framework to the concepts of explicit and tacit knowledge as follows: explicit knowledge is contained in ‘Knowledge repositories and libraries’ (top left in the figure); tacit knowledge is embedded in ‘Communities of knowledge workers’ (top right); meta knowledge is in tools for ‘Knowledge cartography’ (lower box). They term the links between these the ‘Knowledge flow’, which is construed as distributing documents to the right people at the right time.

In the light of what we have said above, you could (by now, hopefully) take the authors to task for the uncritical use of terms such as ‘knowledge flow’ from ‘tacit knowledge’ (we now understand that knowledge may be ‘sticky’ – Brown and Duguid, 2001; and that it may never be possible to ‘convert’ tacit knowledge to other forms), but their contribution is with respect to the role of technologies, rather than epistemology. Remember also that Borghoff and Pareschi's ‘documents’ could refer to a (possibly multimedia) digital artefact which may even be compiled automatically on request from diverse data sources.

The ‘bird's-eye view’ provided by Borghoff and Pareschi's framework encapsulates insights from many other analyses and empirical studies of organisational knowledge, and provides a useful framework for thinking about how technologies relate to each other. It highlights the fact that traditional information systems have a role to play in knowledge management (repositories for codifiable information), but that there are important links to people which emphasise the fundamentally social, tacit, dynamic nature of knowledge as it is generated, shared and analysed by knowledge-intensive communities within organisations. The challenge is to integrate these resources and their interconnections.

In the course of a single task, we may draw on all the knowledge resources shown in the framework in Figure 2, switching rapidly from one to another. For instance, interpreting a stable, formally structured document (for example, a company report or a technical specification) still requires tacit, interpretative skills, which may require access to an expert colleague. Often, such documents are annotated with important notes to the recipient or others (the pervasive phenomenon of the ‘informalisation’ of formal records) in order to situate that knowledge with respect to a particular problem.

As emphasised in Figure 4, it is the timely flow of information between different states that makes it a useful knowledge resource. To adopt a marine metaphor, if expertise gets trapped in any one of the ‘pools’, it stagnates; that is, it becomes out of date and cannot serve as a resource when needed. As we have discovered, the transformation of knowledge representations between different states has implications for what is gained and lost in the power of the representation. See Box 3.1 for an alternative framework which you may find useful.

Box 3.1 An alternative framework for knowledge technologies

A similar but slightly different way of classifying knowledge management technologies is described by O'Leary (1998), who highlights the processes of ‘converting and connecting’. These are summarised below, with references to sections in this unit where we discuss the issues.

Converting individual to group knowledge: knowledge sharing is the assumption underlying models of organisational memory, but it is hard to implement in some cultures, and not straightforward.

Converting data to knowledge: for example, uncovering patterns in databases using data mining (‘Data mining’ in Section 4.3).

Converting text to knowledge: tools for analysing recognisable genres of document and summarising them; tools for evaluating and discussing documents (‘Debating and negotiating meaning’ in Section 4.2).

Connecting people to knowledge: there are many approaches to locating and presenting large amounts of information, including visualisation (‘Information visualisation’ in Section 4.3) and agents to seek out information of interest (‘Software agents’ in Section 4.3).

Connecting knowledge to knowledge: how are knowledge resources interlinked? Agents that can query multiple databases provide one solution (‘Software agents’ in Section 4.3). Ontologies and metadata (‘Ontologies’ in Section 4.3) for describing and interrelating knowledge structures are another route. Boundary objects (‘Communities of practice and technology’ in Section 4.2) help to bridge between communities of practice.

Connecting people to people: maps of who knows what (‘Mapping who knows what’ in Section 4.1) and communication technologies such as telephone, fax, audio/video conferencing, and shared workspaces such as electronic whiteboards over the internet.

Connecting knowledge to people: this again includes agent systems and web-based ‘push’ technologies which deliver to users streams of information such as news summaries and stock prices.

Using Figure 4 as an organising framework, we will next explore the concepts of corporate/organisational memory (Section 3.2), meta-knowledge (‘knowing what you know’ – Section 4.1), tacit knowledge (Section 4.2) and explicit knowledge (Section 4.3), discussing how technologies can support the knowledge resources and processes that the framework suggests.

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3.2 Organisational memory systems

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Without a memory, humans are paralysed in the present moment, unable to reflect on lessons learned or to anticipate the future. You will notice that the heading given to the framework in Figure 3 is corporate memory. The whole dynamic system of people and technologies is conceived as constituting an organisation-wide resource that will enable it to become a more intelligent, learning organism, to pursue the anthropomorphic metaphor. The organisational memory challenge goes beyond traditional information systems design with this much richer conception of collective knowledge processes in the organisation. How can they be integrated, organised and indexed to create a truly useful memory?

The concept of organisational memory proposed by Walsh and Ungson (1991) noted six forms of collective memory. One of these identified the memories (and implicitly the expertise) of individual staff members. Terms such as ‘team memory’, ‘project memory’, ‘community memory’, ‘corporate memory’ and ‘organisational memory’ are now used widely to refer to this human resource plus technological extensions in the form of databases and knowledge bases. While many technologists use ‘organisational memory’ to mean a digital archive, in the light of earlier discussions we would contend that it is meaningless to discuss ‘knowledge archives’ or ‘memory’ in the absence of interpreters. In fact, the term is so loose and widely used that its power lies as much in its evocative imagery as anything else, and it is not hard to construe many, if not all, of the technologies described in this course as playing a role in organisational memory. When it comes to actually designing a system for this, basic questions recur that the designers of any technology intended to support work need to ask: who are the users, what are their tasks, and what are the important cognitive and social processes that enable them to accomplish their work; how can these be supported, and how will they be changed through the introduction of new technologies?

Figure 5

Long description

Often, there is no useful concept of a single ‘organisation’ when it comes to designing memory systems, but rather a collection of interrelated subgroups and communities doing widely differing work. For some strategic planning tasks it may be useful to conceptualise idealised information flows around the organisation as a whole, but this needs to be weighed against the rather ‘messier’ and more complex work processes which such models hide. It may be both more useful and more practical to start with smaller units of analysis than the organisation, reflecting natural clusters of expertise and organisational function such as group or project memory. Once these have been shown to work within a local context, an attempt can be made to link and share information across them. This idea relates to the communities of practice perspective, which prioritises understanding local work contexts before attempting more general characterisations.

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3.3.1 Metaphors for organisational memory systems

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Section 2 argued for a model of knowledge deriving from the situated interpretation of abstract representations. There is an active process by which different interpretations may result from a given information source. This is in contrast to the popular notion that knowledge can be unproblematically encoded and digitally stored and accessed.

Bannon and Kuutti (1996) argue that the term ‘organizational memory’ is widely used to mean a repository based on an implicit ‘memory as bin’ metaphor, whereby material is unproblematically added and extracted. When we look at how human memory actually works, the cognitive sciences show that ‘memory as reconstruction’ is a much better model. Memories are not simply retrieved according to a database model, but are reconstructed in the context of our own understanding of the world, who is asking, and for what purpose. The task for organisational memory design is better conceived as the provision of resources for reconstructing and negotiating meaning (there are often different recollections of ‘what happened’ and different perceptions of ‘what this now means’). This is, therefore, one important difference between a concern with knowledge technologies for human interpretation and action, and databases serving information for machines.

Bannon and Kuutti emphasise the important role that ‘talk’ and ‘narrative’ seem to play, according to a variety of studies into how knowledge is shared in organisational contexts. Although they do not develop this theme's implications for group memory technologies in any detail, the implication is that systems that do not recognise this natural process may not be successful. (We return to this theme in ‘Stories for sharing tacit/informal knowledge’ in Section 4.2.)

A useful ‘design space’ for organisational memory has been proposed by van Heijst et al. (1998) (Figure 6). A design space articulates two or more dimensions that highlight important differences between designs. The authors also suggest example technologies.

Note that both collection and distribution can be active or passive. The metaphorical label in each cell implies the use of very different kinds of technologies. It can be seen that an organisation might make use of all four kinds of system on many scales, from small teams to enterprise-wide collection and distribution.

(Source: based on van Heijst et al., 1998)

Figure 6 A ‘design space’ for organisational memory systems

Long description

Activity 3.1

Consider the concept of organisational memory and the four metaphors in Figure 4: knowledge attic, sponge, publisher and pump.

Which metaphors best characterise the way your organisation's information collection and distribution systems (computerised or otherwise) work (or fail to work)?
Reflect on the use of active/passive criteria in relation to both collection and distribution. Do we assume that the lower-right quadrant is the ideal type, or are there merits in the other quadrants?

Discussion

Most organisations may have elements of all four types, even if these are highly dependent on individuals who act as sponges’, ‘hoarders’ ‘pushers’ and networkers. Certainly, most will have ‘attics’ and many will have ‘publishers’ (for example, the circulation of documents or memos to selected colleagues). We might have some doubts about targeting all our efforts on the ‘knowledge pump’ since its apparent mode of operation is highly dependent on timing. Perhaps we also require a knowledge attic’ where we can store knowledge we are not able to exploit at present; though, ideally, in a form open to search and retrieval by software agents who can ‘pump’ relevant fragments to the right people at the right time (the challenge, of course, is how such agents keep track of the users’ contexts, so that what they send is relevant).

The ‘holy grail’ is a technology which promotes the integration of information and experience in order to build new layers of meaning and higher levels of understanding. Doing this completely automatically is a long-term research challenge, and probably only possible in very tightly restricted fields. A shorter-term target is to produce collaboration tools which mediate and enrich human reflection and discussion, but without overwhelming participants (who only have limited time to engage in such forums) and, ideally, adding value to a conventional online forum by drawing attention to relevant past discussions and contents in the ‘attic’. At present, research laboratories are prototyping such systems: they are not products.

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3.4.1 Integrating memory systems into the flow of work

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There has been a substantial amount of research interest over the last decade in group/organisational memory systems. For example, software researchers have investigated the possibility of capturing design rationale, the key reasoning that underpins design decisions (Moran and Carroll, 1996). However, time and again projects have failed. A given information codification scheme encourages particular ways of thinking about information and the problem at hand: typically, information must be categorised, labelled and perhaps linked to other entries. If this way of thinking does not help the designers in their ongoing work, which is of course their first priority, then the memory system will be rejected. For example, Buckingham Shum et al. (in press) review lessons learned from over 15 years’ field deployment of a particular approach, concluding that there is a fine balance to be achieved between the tool's usability, staff skill and computational services.

Technology vendors would convince us that we need simply install their software and the foundation for organisational memory capture is laid. This completely ignores the whole spectrum of rather more complex human issues concerning how this technology will integrate into the ongoing flow of work in a particular context. It is invariably harder to change work practices than software. Understanding how a memory system will integrate into workflow is critical, as is illustrated by the following two case studies (Boxes 3.2 and 3.3).

Box 3.2 The day-to-day reality of maintaining a team memory

In 1992 Hewlett-Packard researchers developed and tested TeamInfo, a prototype group memory system. They used it for about six months to store information relevant to their project. TeamInfo classified email messages, creating a searchable archive.

Berlin et al. (1993) describe the practical challenges facing this project team. They document the challenge of ‘forced cognitive cohabitation’, in which different team members had to reconcile their idiosyncratic habits and preferences in the way they filed and searched for information. Agreeing on a set of indexing categories was the first step, but it was not enough. People varied widely on at least five key dimensions:

Should items be located in one or many places?
Should ongoing issues be classified under headings reflecting where/when they were discussed, or their meaning/theme, or both?
How fine-grained should subcategories be?
What should be the scope of the repository?
What is the relevant category for an individual item of information? This is determined by the anticipated users of the information and their purposes (different people have different priorities).

Box 3.3 From folklore to living design memory

The Designer Assistant system at AT&T Bell Labs sought to capture the ‘community-specific folklore’ within software development projects in order to assist reuse (Terveen et al., 1993). Previously, such informally maintained knowledge was ineffective (not everyone learned what they needed), inefficient (communication was taking more and more time) and fragile (loss of key personnel meant loss of knowledge).

The solution developed was as follows:

The Designer Assistant provided a user interface to a design knowledge base containing information about an important but complex piece of software which other systems had to call on.
The knowledge base was accessed by designers answering Y/N questions about the design of their systems; experts’ solutions at different points in the hierarchy were stored as textual ‘advice items’.
A record of Designer Assistant use and knowledge base advice was annotated to relevant design documents to allow changes to be traced.
Reports of software bugs and their solutions were encoded in the knowledge base.
Knowledge base maintenance was made part of the development process, and any knowledge base advice which was used was made part of the formal software review process.

Key factors in the Designer Assistant's success were its integration with a widely used information system, the fact that it had enough useful content not to be trivial, and efforts to merge it with organisational procedures to prevent it from becoming out of date or being ignored.

This approach will only be directly applicable to well-defined and understood domains for which a knowledge base can be developed to guide users through the repository's structure. However, ill-structured domains could still benefit from the Designer Assistant's other features if alternative ways can be found to help users submit and index new material.

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3.5.1 Planning a group memory system: a framework

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Nothing can be stored in a computer-supported organisational memory unless it is encoded in some form. Who is going to invest the effort to encode information within an organisation?

Creating a dedicated team of information librarians and knowledge managers is certainly one route, perhaps necessary for long-term maintenance of a large repository, just as librarians are needed to manage traditional libraries. But such a team cannot be experts in all aspects of the organisation's activities, and the people who really need to be managing knowledge are the people who are continually creating and using it in dynamic business environments.

If we consider the scenario of a team that wishes to capture or construct a project memory, numerous issues must be considered. A comparison of two very different group memory systems, deployed in the same organisation, has been reported by Zimmerman and Selvin (1997). Although their focus was software design teams, the issues that emerge are relevant to many other teams. A framework for assessing group memory systems was developed to assist project teams in designing and selecting group memory technologies to suit their needs. First, the concept of a group profile was developed (see Table 3.1) to clarify the important characteristics of the community to be supported. This was then compared with the ‘assumptions/requirements’ row of the framework in Table 3.2.

Table 3.1 Building a group profile when considering a group memory system

Needs	What information does the group need to capture and retrieve?
Size	Number of stakeholders; number of subgroups
Type	What type of project is it?
External	What external groups does this group communicate with?
Phase	What phase is the project in?
Schedule	Does this group have time to learn a new tool/ language?
Budget	Can this group purchase equipment or hire personnel?
Personnel	Does this group have technical writers, developers, leaders, etc.?
Communication	What mechanisms is this group currently using to share their knowledge?
Location	Is this group co-located or geographically distributed?
Skills	Does this group have group memory-related knowledge and skills, such as prior experience with a group memory system?
Motivation	What is team members’ motivation to use a group memory system?
Stability	What is likely to be the duration and stability of the team over time?

(Source: based on Zimmerman and Selvin, 1997, p. 420)

Discussing the framework and building a group profile as a team should help to clarify what assumptions are being made and what their needs are. The team is then in a better position to ask what technologies are required.

Activity 3.2

What would be the costs and benefits of introducing a group memory system into a team of which you are a member or which you manage? Reflect on your group's characteristics and the kind of information they would be seeking to capture and share.

Discussion

Table 3.1 provides a checklist for building a group profile, but you may need to include other factors in order to identify your group's characteristics and information requirements. For example, is the group homogeneous or does it comprise widely different specialists? What is the pace of change in the project domain?

Questions that will help to identify costs and benefits are shown in Table 3.2. One problem is that most of the benefits, and indeed some of the costs, are difficult to identify before a system is introduced. However, all costs and benefits must be evaluated against the costs and benefits of not having such a system. The case study on AT&T Bell Labs (Box 3.3) showed that some of these costs and benefits can be identified.

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3.6.1 When we just want to forget (‘we're only human’)

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Group memory systems might be counterproductive if they damage morale or prevent a team from moving on after a failure. Studies of software teams show that many commercial projects are cancelled before completion. This generates an intense pressure to work as hard as possible (so that maintaining group memory falls by the wayside) and, understandably, in many cultures if a project is regarded as a failure everyone wants to forget it as quickly as possible rather than analyse it for lessons learned or to record design rationale (Grudin, 1996). It is likely that this is the case in many other domains as well. However, the investment of knowledge in such projects, and the lessons learned, can be extremely valuable if they are recorded in an organisational memory system. We are thus faced with a very human obstacle: how can a project team be expected to document its failures?

Team memory might also be counterproductive or even threatening if it becomes ‘good for the organisation but not necessarily good for the individual’ (Conklin and Burgess Yakemovic, 1991, p. 389). Consider the following issues that the authors raise in relation to recognising failure:

Who does the routine work of knowledge capture?
How is politically or personally sensitive information handled?
How are staff who were honest enough to document their wrong turns and bad ideas rewarded and protected?
What prevents a group memory system from being used against staff in the event of litigation stemming from a poor decision?

Table 3.2 A framework for assessing and planning a computer-supported group memory system

Categories	Tasks
	Setup of group memory system	Information input	Information formal isation	Information retrieval
Definition	What are the steps to be taken to set this group up with the group memory system tool?	What do the various users of the system need to do to enter information into the system?	What mechanisms are available to formalise the information? What can users do to help the system's automatic formalisation features work better?	Who is expected to retrieve information from this system (group members, external groups, future groups)? What mechanisms are in place for this retrieval?
Assumptions/ requirements	What are we assuming about the group as outlined in their profile? What work procedures are required for users to begin using this system? What are the expectations of their current work practices?	What is assumed about the user community in order for them to enter information into the system? Training, motivational factors, time constraints, group size, etc. should be considered	What is assumed about the user community in order for them to enhance the formalisation of information in the system? Training, motivational factors, time constraints, group size, etc. should be considered	What does the user need to do to retrieve formalised data? What is the user willing to do to retrieve that information? Learning a new language, information overload issues are some things to consider
Costs	What is the cost associated with setting up the system? Training, system setup, hardware requirements, etc. should be considered	What cost is associated with inputting information into the system? Things outside the existing work practices of the users should be included (extra time required, software, etc.)	What cost is associated with formalising information in the system? Things outside the existing work practices of the users should be included (extra time required, software, etc.)	What cost is associated with retrieving information from the system? Information overload, lost information, learning a new language, learning new query mechanisms, etc. should be included
Benefits	What direct/indirect benefits does the group obtain from setting up this system? Defining a group's structure, learning new ways of communicating, solving problems, etc. should be included	What immediate benefit does the user gain by inputting information into the system? Consistency in communication, gaining a deeper understanding of a problem, learning a new way to communicate, etc. should be included	What immediate benefit do users who formalise information obtain? Clearer understanding of group tasks and goals, clearer group understanding from using structured language, etc. should be included	What benefit do users obtain from searching for and finding information in the system? What value does the memory have to group members and non-group members? What value is there in looking for information?

(Source: based on Zimmerman and Selvin, 1997, p.419)

In summary, while building organisational memory is one of the most widely proclaimed goals of knowledge management, co-designing the technologies and human dynamics (both cognitive and social) to enable meaningful capture, indexing and reuse is far from straightforward.

We turn now to specific examples of technology which can support varieties of knowledge management processes. These are related to the different kinds of knowledge presented at the start of this section – meta-knowledge, tacit knowledge and explicit knowledge, and their complex interplay.

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4.1 Technologies and meta-knowledge

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Meta-knowledge is knowledge about knowledge; for example, ‘I know that I know my age’. Meta-knowledge is crucial for managing our own learning and knowledge. For instance, I need to be able to recognise that I am lacking information before I will go and seek it out.

Not surprisingly, meta-knowledge is also crucial to organisational knowledge management. How can an organisation coordinate its activities or learn from the experiences of its members if it has no idea of what it knows? Figure 7 shows a 2 x 2 matrix derived from asking the question ‘Do you know what you know?’ Relate this for a moment to your own work situation.

Source: Jordan et al., 1998, p. 95

Figure 7 Do you know what you know?

Long description

Two kinds of meta-knowledge that can be supported by ICTs are who knows what? and what kinds of knowledge do we value?

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4.1.1 Mapping who knows what

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One of the most widespread ways to represent what you know is to represent who knows what. This avoids the complications of codifying or storing the knowledge in great detail – you simply map the relevant people to a high-level taxonomy, leaving them to give contextualised answers when asked. Initiatives to provide corporate ‘yellow pages’ which map an organisation by what people know rather than by where they work, or alphabetically, have been reported to be extremely popular and successful. One problem with this approach is that individuals may be inundated with requests for help which they are required to address in addition to their ‘real work’. A solution which some organisations are now following is to make knowledge sharing a priority by recognising and rewarding ‘information gatekeepers’ for different areas.

The most basic form of knowledge map is a listing of resources, both people and documents, on paper (see Box 4.1) and/or delivered as web pages. Such ‘portal’ sites proliferate on the Web, compiled by enthusiasts, companies and search engines to point to key resources in one or more fields. Although technically simple to implement in its most basic form, such a resource requires a dedicated person or team to keep the taxonomy of categories and descriptions of people up to date. Some companies with dedicated knowledge managers, librarians or information services may distribute a standard set of intranet and internet ‘bookmarks’, which makes available to staff a coherently organised set of information sites directly from their desktop. Of course, this can also happen on an informal level as colleagues swap new discoveries.

Box 4.1 Natwest's Green Book

While working at NatWest Markets, a member of staff developed a compact booklet called the Green Book which listed staff under areas of expertise, rather than by name or location. The goal was to enable staff to locate a suitable subject expert by no more than two phone calls. The book had a very good reception and has spawned countless similar initiatives. Note that no computing technology was used in this case except to collect information and lay out the book.

Clearly, we can imagine the advantages of having a dynamically updated information system which would not need to wait for the following year's release before it could be modified. However, this needs to be weighed against the advantages of paper: it is portable, it never crashes, it is easily annotated, and so forth. Paper still wins over screens in many work environments.

Generating a knowledge map from an underlying database is a more manageable solution, and makes possible integration with other systems. The site can be augmented with staff photos and even video clips to help establish relationships between geographically distant staff who need to consult each other.

Davenport (1998) has written an informative article entitled ‘Ten principles of knowledge management and four case studies’. In the case studies he describes several initiatives at Hewlett-Packard. One example (Box 4.2) illustrates that knowledge sharing requires more than simply installing websites and discussion forums, and that even material rewards for contributing to such repositories had a limited impact.

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4.2.1 Mapping who knows what continued

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Box 4.2 Knowledge sharing at Hewlett-Packard

One knowledge management initiative involves HP educators. Bruce Karney is a member of the infrastructure team for the Corporate Education organisation, part of HP's Personnel function. Karney estimates that there are more than 2,000 educators or trainers distributed around HP, most of whom work within small groups and find it difficult to share knowledge. About two years ago, in response to complaints by the education community that, ‘we don't know what's going on’, Karney began work on approaches to knowledge sharing for HP educators. He hoped to make the group more of a community; until this effort, it had no shared history, process, or tool set.

Using Lotus Notes as the technology vehicle, Karney established three different ‘knowledge bases’ for educators to use:

Trainer's Trading Post, a discussion database on training topics
Training Library, a collection of training documents (e.g. course binders)
Training Review, a Consumer Reports collection of evaluations of training resources.

Training Review never took off; educators were reluctant to opine on-line about the worth of course materials or external providers, and there was no reward structure for participating. It was therefore merged with Trainer's Trading Post. Training Library did receive many contributions, but as participants discovered that they could attach materials to submissions to Trainer's Trading Post, that knowledge base became the dominant medium for educator use, and Karney expects that it will be the sole offering in the future.

Karney adopted innovative tactics to get submissions to the knowledge bases. He gave out free Notes licenses to prospective users. When a new knowledge base was established, he gave out 2,000 free airline miles for the first 50 readers and another 500 miles for anyone who posted a submission. Later promotions involved airmiles for contributions, for questions, and for responses to questions. By early 1996, more than two-thirds of the identified educator community had read at least one posting, and more than a third had submitted a posting or comment themselves. Still, Karney was frustrated. Despite his countless attempts with free miles and e-mail and voice mail exhortations, he still felt the need to continually scare up fresh contributions. ‘The participation numbers are still creeping up,’ he notes, ‘but this would have failed without an evangelist. Even at this advanced stage, if I got run over by a beer truck, this database would be in trouble.’

(Davenport, 1998, p. 192)

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4.3.1 Mapping what we know

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Knowledge maps are often one of the first knowledge management representations to emerge, in an effort to add value over the simple corporate intranet search which returns lists of ‘hits’ that are undifferentiated beyond a ranking in terms of keyword matches. Knowledge maps, like other forms of cartography, should communicate a ‘big picture’ by overlaying meaningful structure on to raw resources.

Box 4.3 Information cartography

A company called Dynamic Diagrams (www.dynamicdiagrams.com) has many examples of techniques to manually map the structure of a website (or, by extension, knowledge resources in an organisation's intranet), as well as novel techniques for mapping documents or sites based on automatic analysis of their content.

Top down or bottom up?

Activity in this field sometimes comes under the banner of organisational knowledge taxonomies. Such taxonomies are typically defined by a specialist group in what we might call a ‘top-down’ manner: defining a set of a categories which must then be used in a systematic manner to classify material. However, a cautionary note is sounded by Davenport for those who believe they can unilaterally define a useful ‘master taxonomy’ for all staff:

It is tempting when managing knowledge to create a hierarchical model or architecture for knowledge, similar to the Encyclopaedia Britannica's Propaedia, that would govern the collection and categorisation of knowledge. But most organisations are better off letting the knowledge market work, and simply providing and mapping the knowledge that its consumers seem to want. The dispersion of knowledge as described in a map may be illogical, but is still more helpful to a user than a hypothetical knowledge model that is best understood by its creators, and rarely fully implemented. Mapping organisational knowledge is the single activity most likely to yield better access.
Knowledge managers can learn from the experience of data managers, whose complex models of how data would be structured in the future were seldom realised. Firms rarely created maps of the data, so they never had any guides to where the information was in the present.

(Davenport, 1998, p. 189)

In contrast, a better strategy is a user-centred design approach, which would typically consult representative user groups to uncover their most common information needs (how do they think about their world?), possibly ask users to build an actual map of their world view using a card sorting exercise, and normally conduct evaluations of the current search engine to see how well it performed. However, it is also well known in the field of user-centred software requirements analysis that end-users do not always know what they want (echoing Polanyi, 1966 : ‘we can know more than we can tell’), and, moreover, new tools can change the way people behave by presenting opportunities they did not imagine. Think about how we rapidly recover known information sources on the internet with search engines – it is often quicker to type in a few keywords in a search engine toolbar than to take the trouble of manually bookmarking the page, or retyping the full address if known. The answer to this dilemma is in planning time for deploying a series of prototypes and evaluating the emergent patterns of usage – patterns which neither users nor designers could foresee in advance, but which are a function of the specific user group working under the demands of their unique contexts.

Human- versus machine-generated metadata?

Metadata about the contents of an information resource, or the way in which people behave, can come from people or computers. The contrast is between (human) declared structure or (machine) inferred structure. To make this clearer, consider some examples:

Portals (topic-centred websites with categorised, validated links to relevant information). The categories can be either predefined by the portal's designers, or automatically clustered by analysing the content of linked information.
E-commerce website customer profiles. Customers can either select their shopping interests from a predefined list of topics, or the site can try to analyse their interests by tracking their purchases, and inferring what other kinds of products they might buy.
Document/news classification. Users can either be asked to assign keywords that provide a machine-readable summary of the document or news item (such as terms selected from a controlled vocabulary), or the system can try to analyse the text and build an abstraction of its ‘meaning’.

The advantages of asking humans to classify and abstract is that, on a case-by-case basis, they may do a better job than a machine (but someone has to define the categories: staff have biases, an incomplete awareness of who may want to find the document, and are notoriously poor in the use of complex taxonomies). Metadata from trained librarians or information scientists (typically employed only in large organisations) is likely to be high quality in terms of consistency and coverage. Metadata from other staff may still be useful, but vary in quality.

The advantage of having machines infer the ‘semantics’ (meaning) of texts or images is that they are not so vulnerable to these human traits, and can more easily keep up with the changing information space as new material is published. Tools concerned with text mining, information extraction, thesaurus generation/maintenance and ICT network usage analysis will continue to grow in sophistication. Machine-generated analyses of raw texts, images and user activity patterns, therefore, may be a promising way to support the construction of meta-knowledge that can answer questions such as, ‘what do we know?’, ‘what's out there?’, and ‘what do people do?’, since they do not require people to modify (and perhaps change) their behaviour by explicitly categorising their work products or processes. However, machines are, of course, restricted in their ability to make sense of artefacts and processes, having an extremely small porthole on to the rich human world. If a machine can do 75 per cent of the summarising or classification work that a human can, this may or may not be adequate (compare classifying news stories about competitor products with classifying intelligence about terrorist activities).

The answer, typically, lies in carefully designed hybrid systems that release machines and people each to do the things they do best, to an appropriate performance threshold. ICT can analyse a network and cluster candidate metadata terms for a corporate portal designer to edit. ICT can also suggest alternative synonyms to an untrained user entering keywords, in order to match them to a corporate taxonomy. Users should be able to suggest new terms that they find meaningful in their context, but it normally needs a taxonomy/ICT expert to add this to the official taxonomy.

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4.4.1 The map isn't the territory

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

The expression ‘the map isn't the territory’ draws attention to the difference between complex reality and simplified models of it. Normally, the territory is relatively stable and different maps are produced for different purposes; the territory shapes the maps, not vice versa. However, when the ‘territory’ comprises people who know that they – or their work activities – are being mapped, we find ourselves in a reflexive loop: the people can see how they and their work are being mapped and (if they care) they may well change in response to this: and so the map in turn needs to be updated.

Historians have shown us how political cartography is, and this is equally true when mapping organisational structures and priorities. The introduction of systematic knowledge management (whether or not technology is involved) creates a new economy of knowledge and a knowledge vocabulary. Creating a map of corporate knowledge categories does precisely this. Any group and their work will remain invisible, and thus unresourced, unless they can position themselves within this new economy, using the right language (and the right metadata).

Bowker and Star present an illuminating analysis of the impact of ‘professionalisation’ – systematic classification of skills and courses of action, and management of these via technology – on nursing, a profession in which much of the most valued expertise is a craft skill that is hard to codify:

One of the main problems that… nurses have is that they are trying to situate their activity visibly within an informational world which has both factored them out of the equation and maintained that they should be so factored – since what nurses do can be defined precisely as that which is not measurable, finite, packaged, accountable.

(Bowker and Star, 1999, p. 265)

This illustrates the political dimensions to formal classification. The names and labels used unavoidably emphasise particular perspectives. The map that an organisation creates may therefore trigger unforeseen changes. Of course, there are organisational documents and charts that are ignored by staff. However, Davenport and Prusak (1998) warn that if a knowledge map does not cause some controversy it is a sign that it is not being taken seriously by the very people who should be ‘owning’ it, and this raises questions about how the knowledge management initiative is being implemented.

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4.4.2 Mapping across multiple communities of practice

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

In introducing the core concepts, we highlighted the perspective that ‘what counts’ as valuable knowledge is unavoidably shaped by the communities of practice to which the ‘publisher’ and ‘consumer’ belong. One makes situated judgements regarding the relevance of a new piece of information for oneself and others, and how to store or share it appropriately. One geographical metaphor conjured up by this perspective is that of ‘islands’ of local coherence, with narrow ‘causeways’ connecting them (interchange of information), and with particularly talented ‘linguists’ who can speak more than one island's language, possibly even holding ‘dual nationality’ such that they can move comfortably in more than one culture. In particular, there may be no mapping scheme or level of detail that can usefully describe material across this whole ‘archipelago’ of islands. Such a map is either so general that it is of limited use, or too specific, imposing the language and distinctions of a particular island. From a meta-knowledge perspective, this is a serious challenge to gaining a meaningful bird's-eye perspective of an organisation.

In this context, an interesting strategy (deployed, for example, in IBM – see Snowden, 2000) is to leave individual communities to negotiate among themselves how to respond to a request for information from an outsider. This takes seriously the idea that boundaries are important to the building of trust and expert practices, while recognising the need for organisation-wide communication and search. Groups are provided with genuinely private virtual workspaces (documents; discussions; video conferencing; messaging; etc.), knowing that they are not going to be spied on by outsiders, and free to negotiate their relationships with outside groups, taking into account organisational sensitivities that are unformalisable. Forcing individuals or groups to share knowledge against their will, thus violating these principles, results in what Snowden (2000) has dubbed ‘camouflage behaviour’ – vacuous ‘information publishing’ or ‘knowledge sharing’ in order to retain privacy, autonomy and invisibility.

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4.5 Technologies and the tacit dimension

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

In this unit we have discussed the intriguing notion of tacit knowledge, or perhaps better, knowing as a situated process. What might it mean to provide technological support which exploits the tacit dimension? If ‘tacit’ can mean ‘not yet codified, but could be’ in Nonaka and Takeuchi's (1995) sense, then we can devise computer systems that assist in formalising information and ‘transforming’ it into explicit, shared knowledge to feed the knowledge spiral. However, if ‘tacit’ means ‘intrinsically uncodifiable’ in Polanyi's (1966) sense, what is the role of digital technology which depends on a symbolic codification scheme?

The answers lie in the level of abstraction at which we strive for symbolic coding. At the lowest level, everything is digital: a 1 or a 0. However, few people think at this level. As we layer abstractions on to this base layer, we enter into the symbolic codification process described earlier. The question is when to stop: when does it no longer make sense to codify information chunks into abstract categories?

One approach is simply to switch from trying to formally model the world (such as taxonomic categorisation of information, or trying to recognise classes of behaviour such as writing a letter of a particular sort in order to offer ‘intelligent’ help), and focus instead on augmenting people's ability to use and share their own tacit knowledge while engaging in their work. The emphasis here is on the computer as communication and collaboration medium. By focusing on augmenting knowledge-intensive communities with richer forms of communication, we certainly sidestep the problem of ‘codifying the uncodifiable’, but also the lesser problem of ‘codifying the hard to codify’.

The objective of virtual collaboration tools is to link people separated in time and space by appropriate communication systems, enabling them to continue drawing on their tacit knowledge with minimum disruption. Often, these systems are termed groupware, computer-mediated communication (CMC) or computer-supported collaborative work (CSCW) systems. Lotus Notes and The Open University's Lyceum system are two rather different examples. Lotus Notes (www.lotus.com) is one of the most widespread groupware systems. Notes provides integrated facilities for email, discussion groups, scheduling and web intranet services, integrated with databases and other systems.

Another approach to the challenge of augmenting tacit knowledge with technological support is to assume that tacit knowledge is brought to bear as and when it is required. We will look at systems that seek to help users record information as it comes to mind while engaged in a task.Simulations are yet another strategy for fostering tacit knowledge and skills. Consider flight emulators (embodied games) and management games (decision-making / team skills).

Stories are a common and enjoyable way in which we communicate experiences to friends and colleagues, so it would be surprising if they did not have an important role to play in the sharing of tacit, informal knowledge within and between organisations. How might we use technologies to recognise and support storytelling?

Finally, at the end of this section we shall return to the theme of communities of practice introduced earlier as a core concept, and consider some technological implications of locating these workplace communities at the centre of how we understand knowledge generation and sharing.

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4.6.1 Connecting people to people

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Compared to even five years ago (a long time in technology), tools for virtual meetings and workspaces are extremely common now in many organisations, who typically purchase specialist products rather than develop their own. Tools for virtual meetings really have to work smoothly or the results are immediately obvious, and can be very high cost (for example, one cannot afford for a meeting with an important client to ‘crash’). Organisations are therefore willing to pay for robustness, 24/7 technical support, and integration with intranet databases so that, for instance, staff in a personnel database can be accessed directly from collaboration tools. An internet search on ‘enterprise groupware’ will give you an up-to-date listing of ‘off the shelf products, some of which you may already use to share calendars and documents, and to conduct online meetings.

Meanwhile, of course, academic, government and corporate research laboratories continue to investigate next generation tools, examples of which are given in Boxes 4.4, 4.5 and 4.6.

Box 4.4 Interplanetary collaboration tools for NASA scientists

NASA is planning missions for human exploration of Mars in 20 to 30 years’ time. The specific challenge is for the crew on Mars to work effectively over a period of months with the many scientific experts distributed around Earth who can advise them on their exploration plans. ‘Mars’ is simulated by a crew living in a prototype habitat in the Utah desert, which they explore for two weeks, and with whom all communications are artificially delayed by 15 minutes to simulate the distances which preclude real-time collaboration of any sort. The Open University was invited to work with NASA on this extreme challenge through the use of its collaboration tools designed to bridge geography and time zones (Clancey et al., 2005).

The Remote Science Team (RST) of experts cannot be co-located for months on end to support the mission, so a virtual science collaboration environment is needed to enable them to work effectively across time zones. One of these tools assists in maintaining a ‘sense of presence’ plus instant messaging for rapid exchanges. Screen 1 (see below) shows The Open University's BuddySpace tool configured for the RST.
The Open University's Compendium hypermedia tool for visual modelling and dialogue mapping (see Box 4.11, and the article entitled ‘Rapid knowledge construction: a case study in corporate planning using collaborative hypermedia’ by A. Selvin and S. Buckingham Shum is being trialled as support for the RST to conduct virtual meetings and manage their knowledge (conventional Earth-based collaboration), and as a means for Earth-Mars collaboration. A link to this case study is available below. Screen 2 (see below) shows an extract from such a meeting.
Given the communication delay between Mars and Earth, the usual electronic ways of working together at a distance, such as telephone, instant messaging and the sharing of computer screens, are impractical. The objective was to enable the RST to ‘attend’ a crew meeting, so that they could gain a better understanding of the rationale behind the crew's plans. A multimedia Meeting Replay extension to Compendium was developed which combines meeting materials within an interface structured to enable quick and easy indexing navigation of the meeting record (little value would be added for the time-pressured RST if they first had to watch two hours of video). Scientists could then watch clips of the meeting together or individually, browse the video by speaker or agenda item, and click on a node in a Compendium map (for example, an Argument icon challenging a particular plan) to jump directly to the point in the meeting video when this argument was made, in order to hear it in full detail, with all the richness of the contributor's voice and gestures: that is, the context. Screen 3 (see below) shows the web-based Meeting Replay tool.

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Long description

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Screen 1 The Open University's BuddySpace system (www.buddyspace.org), for NASA science teams distributed around Earth. The red and green dots indicate availability for instant messaging (right window), and can be superimposed over any geographical or organisational map

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Screen 2 Using Compendium to support a virtual meeting between a NASA science team on Earth, providing a shared display of the emerging Questions, Options and Criteria

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Screen 3

Screen 3, above, shows the NASA Meeting Replay tool to help the Remote Science Team (RST) team on Earth recover the rationale behind the Mars crew's analysis and decisions. The upper region shows the video of the meeting and the Compendium map (Box 4.11) as the discussion progresses. The lower region contains summary information about the meeting: who was there, who was speaking, the agenda and an overview of the current topic (derived from the Compendium map). Some of this information is presented as a timeline, providing a visual index for an RST member to navigate the video, jumping to relevant or interesting parts of the discussion by clicking on the timeline or moving the slider. One can also click on a node in a Compendium map and the replay jumps to the point in the meeting shortly before that node was recorded. (Acknowledgements to Maarten Sierhuis and Bill Clancey, NASA Ames Research Center, and Dave De Roure and Danius Michaelides, University of Southampton)

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4.7 Technologies and the tacit dimension continued

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Box 4.5 Technology briefing: audiovisual Webcasting

The emergence of the internet and private, higher-capacity corporate intranets makes it possible to ‘broadcast’ over digital networks, saving time and money since staff do not have to physically gather in one location. The term webcasting is used to describe web-based audiovisual broadcasts. These enable individuals to make (for instance) slide presentations to staff dispersed all over the world. All that is needed to receive them is access to the Web via a web browser, possibly enhanced with special ‘plug-ins’ (software extensions), depending on the particular technologies used to encode audio, video and slides.

The Open University's Knowledge Media Institute (KMi) has been developing its own webcasting technologies which it has deployed in both university and corporate contexts. Screen 4 shows a user interface for receiving a live or replayed Stadium webcast.

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Screen 4 Viewing a webcast using The Open University's KMi Stadium system (stadium.open.ac.uk/podium). The slides change as the speaker changes his or her slides, or the audience member can click ahead using the menu on the left

Box 4.6 Technology briefing: internet video conferencing

Video conferencing over the internet or intranet is becoming increasingly commonplace thanks to robust, usable new tools. Many people first encountered it using the NetMeeting tool built into Microsoft Windows. Plug in a webcam and microphone, and you could hold two-way video conferences which could be expanded to multi-way conferences using special extensions. Video conferencing is now a standard offering in the freely distributed instant messaging clients offered by Microsoft, Yahoo, AOL and many other internet providers. Other vendors such as Macromedia offer a server which allows developers to build in audio/video conferencing to their own applications very easily. See, for example, The Open University's FlashMeeting system (www.flashmeeting.com), which enables the booking and recording of video conferences with the only requirement on end-users being a web browser with the standard Macromedia Flash browser plug-in (as well as a webcam and microphone, of course). These tools emphasise ease of use for the end-users direct from their desktop or laptop, but to do so sacrifices quality in some respect: video images are still quite small, or it may be possible for only one person to speak at a time. Flashmeeting is currently available for use in OpenLearn.

A different emphasis is sought in the emerging Access Grid environment (www.accessgrid.org) which is being developed to exploit the next generation internet for science and business, called the Grid. The Access Grid provides large, high-quality video images and full-duplex audio (people can speak over each other). Special conferencing rooms can be set up which are designed to maximise the quality of audio and video, with high-quality cameras, projectors and microphones.

Stepping back a few paces, let us think about the space of possibilities opened up by tools of this sort. Figure 8 shows a simple framework for relating different groupware systems, with some examples of technologies included. Many of the most popular intranet groupware systems currently being adopted for knowledge management, such as Lotus Notes, are essentially helping dispersed staff to communicate synchronously (different place/same time) or asynchronously (different place/different time).

(Source: based on Grudin, 1994a)

Figure 8 A matrix of technologies for supporting communication across time and place

Long description

Extensions can be added to Figure 7 if other dimensions are also important to a comparison of systems. One is whether the place and time are predictable or unpredictable; another is how many people are sending information and how many are receiving. Thus, if we take the example of a shared electronic whiteboard on an intranet or the internet (same time/different place), we also have the option of a single author broadcasting their whiteboard (from one) to just one recipient (to one), or to many users all tuning into this ‘broadcast’ (to many), each of whom might also be able to annotate the whiteboard, turning it into a ‘multicast’ (from many/to many). This is summarised in Figure 8.

Activity 4.1

Think about the main technologies that support communication in your work and place them in the matrices in Figures 7 and 8.

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4.8.1 Capturing meetings

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Internet meetings and broadcasts can be easily recorded and replayed because everything is mediated digitally: the text of emails, the audio stream and the slides used. However, face-to-face meetings are by far still the most common way to present and discuss issues in organisations, and the richness of personal presence makes them unlikely to disappear. How can face-to-face meetings be ‘captured’? Traditional written minutes provide a rough summary of points discussed, but provide only the rapporteur's understanding and do not capture the context in which someone said something, or the gestures and expression which accompanied it.

(Source: Repenning et al., 1998, p. 6)

Figure 9 For each cell there is a matrix of technologies showing how many people are sending and receiving information

Long description

Unobtrusively capturing and then browsing records of what happens in face-to-face meetings (in real or video space) is a significant challenge. Obviously, we can simply set up an audio tape recorder and record everything that is said, or a video camera and also capture gestures and movement. However, viewing two hours of video to revisit a discussion is prohibitively time-consuming.

Experimental tools are being developed which create a structured, digital record of a meeting. As these mature, users will be able to browse these records along a timeline, via markers that meeting participants have added to flag different kinds of events as they occur (for example, ‘relevant to System X’). With new electronic whiteboards, participants have available to them an infinitely large whiteboard (any number of screens can be saved) and the familiar pen input device. (SMART Board (www.smartboard.co.uk) is an example of an e-whiteboard product. eClass (www.cc.gatech.edu/ fce/ eclass) is an example of a research system that also captures activity surrounding e-whiteboard work.) Advanced systems enable activity associated with whiteboard entries to be captured via digital audio and video records. It is possible to play back what was being said when a particular note or drawing was edited, or search for a particular utterance and see what was happening on the whiteboard at the time (Moran et al., 1997; Abowd et al., 1998).

This section has been focusing on technologies for linking people to people. A widely cited article on groupware failures by Grudin (1994b) analyses a number of key differences between designing single- and multi-user systems. Grudin's empirical analyses of failures in a variety of groupware applications led to the identification of eight challenges for groupware design.

Disparity in work and benefit: groupware often requires additional work from people who do not perceive a direct benefit from using the system.
Critical mass problem: some group applications really only work when a ‘critical mass’ of people use them.
Disruption of social processes: group applications may break existing social rules and roles within an organisation or institution.
Exception handling: group interaction is very complicated and a lot of repair and improvisation may happen; applications often fail to accommodate this.
Unobtrusive accessibility: groupware introduces important, but infrequently used, features (for example, privacy settings) which must always remain accessible to users without distracting them from their real work.
Difficulty of evaluation: because of the number of people involved, and the cultural embedding of the interaction, it is very hard to evaluate collaborative systems properly and learn from experience.
Failure of designers’ intuition: intuitions about multi-user applications are especially poor in product development environments, resulting in bad management decisions and design errors.
The adoption process: group systems need to be introduced into workplaces much more carefully than single-user systems.

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4.9.1 Stories for sharing tacit/informal knowledge

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Once war stories have been told, the stories are artefacts to circulate and preserve. Through them, experience becomes reproducible and reusable.
[War stories] preserve and circulate hard-won information within the community.

(Orr, 1990b, pp. 156, 157)

We all recognise that stories are one of the most natural and compelling ways to exchange experiences. Many commentators have noted the important place that stories hold in every culture, ancient and modern; humans from the earliest age seem to be ‘wired’ to share them. Stories, or ‘narrative forms’, have been the subject of research interest in cultural anthropology and literature, and in cognitive psychology and organisational studies.

As stories are an essential process by which culture and knowledge are shared among staff, the obvious question arises regarding their potential for managing knowledge.

Narrative technologies

If a veteran member of staff leaves, they take with them their accrued wealth of stories. Is it possible to provide technologies that would make it easy to record stories?

One of the simplest technologies that has been used for sharing stories is basic voice contact. Orr (1986, 1990a) has described how field service engineers (in this case photocopier engineers) used radios to consult colleagues when facing difficult problems (today it would more likely be mobile cellphones). This relatively mundane but highly effective technology enabled them to continue the exchange of ‘war stories’ that Orr found to lie at the heart of the engineers’ conversations when they met face-to-face. Translating this to a broader context, we should not forget that the telephone, especially with the availability of mobile phones, may be one of the most potent knowledge-sharing technologies. Box 4.7 describes how this pioneering work inspired a follow-on system.

Box 4.7 From field stories to a community memory system

Orr's study of storytelling among service technicians led Xerox to implement the Eureka project, in which stories are shared electronically. Technicians are motivated to share stories through the recognition they gain within their community of practice. Xerox report more than 5,000 ‘tips’ being posted per month, 15 per cent being ‘validated’ and adopted by the company, and a 10 per cent improvement in costs (Cross, 1998). Perhaps the most interesting aspect of Eureka is not in fact the technology, but the system's design process in which service technicians were heavily involved.

More recent developments suggest a role for digital audio/video-based tools in supporting the personal, social nature of storytelling. Easy access to such media enables staff to reflect on a project or relate their experiences with a particular problem or client. Projects can capture recollections of key points and lessons learned as indexed, digital video clips which can be easily navigated and subsequently annotated. Screen 5 is from a prototype multimedia environment which allows the end-user to browse a project history and the lessons learned. It is fair to say, however, that such multimedia archives are still relatively rare. ‘Low tech’ is often better for wide dissemination, and Box 4.8 describes a successful initiative which uses a paper/web magazine format.

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Screen 5

Screen 5, above, is from a ‘project story’ CD-ROM produced by a design team as a group memory and organisational learning resource. Members of staff reflect on what they thought the key lessons were. With the right authoring tools and framework, this kind of multimedia resource can be created rapidly (Carey et al., 1998). Note that the user may browse the storybase from the different perspectives of team members (the photos), or via the project timeline (foot of screen), with tools to write personal notes and reflect on how they did things (icons in right margin)

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4.10 Technologies and the tacit dimension continued

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Box 4.8 NASA knowledge brought to life in a story portal

A non-technical approach has been adopted within NASA. It was found that seasoned engineers, astronauts and other staff had memorable stories of lessons learned, but which were poorly known. In addition, even with their knowledge, not everyone was a great storyteller. A journalist was hired to interview staff and re-present their stories in a magazine format, which was also made available on the Web. This has evolved into the award winning ASK (Academy Sharing Knowledge) magazine, which also enables the user to view related stories by ‘Lesson Learned’.

Activity 4.2

Think about the most useful experiences that colleagues recount at work. Could they be captured in a video database, or are they too sensitive? Could they be adapted to give them an acceptable public face while still retaining some of their utility? Could you see a version of NASA's ASK magazine working? What could you hear your colleagues saying if they were invited to publish their stories?

Discussion

Capturing stories for posterity clearly raises ethical as well as utilitarian questions. The ways in which stories are ‘captured’ and permission given for their use needs to be properly handled. The 30-second story shared in the lunch queue is unlikely to work on a website without adding some background and elaboration. But the investment of time and resource to craft someone's account into an engaging article is also a public recognition of the value of their experience – it is worth a little effort to give it a longer ‘shelf-life’.

Stories are just part of the social fabric of the workplace. Box 4.9 introduces other forms of social software which are beginning to shape the workplace, physical and virtual.

Box 4.9 Technology briefing: social software

Several emerging phenomena on the internet fall under the banner of social software. These tools are proving their capacity for supporting ‘lightweight’ communication (i.e. relatively low effort on the part of the user). Social software covers tools such as:

Instant messaging (also known as textchat) enables rapid exchanges of messages. All the big internet service providers provide free messaging clients, and enterprise-wide ICT tools such as Windows and Notes provide this built in.
Blogging (originally ‘web logging’) is a form of web diary in which an author adds comment/interpretation about a current issue, problem, or proposed solution. Blogs can be automatically subscribed to each other, so that authoritative authors start to be ‘syndicated’ very rapidly.
Workspaces support teams who need to share documents, or collaborate asynchronously or synchronously.
WIKIs are web pages which are editable by everyone in a group (as tightly or loosely defined as required), in contrast to normal web pages which are only editable by one person. These are finding wide application as project workspaces.
Presence indication can be embedded in any of the above, showing one's colleagues’ location and availability for communication through a simple icon (BuddySpace and Screen 1), or a low-fidelity video image (Hexagon).

These modes of communication are not only sweeping the internet for social use, but making inroads into organisational life in some of the biggest companies, some of which have cultures permitting the installation of open source tools, while others wait to adopt the tools once they are embedded in large enterprise-wide products. Even if you do not use these much, teenagers already spend a lot of time immersed in such tools (and as your future workforce, they will find such modes of interaction second-nature).

One of the reasons these tools are so popular is that they do not require users to make explicit any form of metadata, or subscribe to an ontology (see Section 4.3). In the research laboratories, however, hybrid systems are being built which add a layer of more explicit ‘semantics’, to investigate the synergies of combined human/artificial intelligence. Websites already exist where the users are free to define their own keyword tags, but who then start to adopt other peoples’ when they see that they are ‘gaining currency’ as a way to expose content (a ‘bottom-up’ version of our earlier discussion of how a new knowledge vocabulary can be set up by the declaration of a corporate taxonomy). Terms such as ‘semantic blogging’, ‘WIKIs’ and ‘messaging’ in the next few years have become increasingly widespread recently.

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4.11.1 Debating and negotiating meaning

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

The two briefings in Boxes 4.10 and 4.11 illustrate other technological approaches to supporting socially based forms of knowledge generation, with the common theme of facilitating negotiation and debate among stakeholders. These are examples of tools which can assist communication between communities of practice as they seek to understand each other's perspectives.

Box 4.10 Technology briefing: enriching documents with annotations and discourse

Since knowledge will exist in multiple degrees of explicitness and formality, computers need to support this. A ubiquitous phenomenon is the informalisation of formal representations of knowledge, as mentioned in Section 3.1. This is the natural result of interpreting and contextualising a static document to your particular requirements. The tangible result often takes the form of annotations all over a formal document, and discussions about that document, either online or face-to-face. Many office and intranet products now provide annotation and discussion facilities as sharing documents over networks becomes the norm. The widespread availability of pen-based computing such as Tablet PCs takes us a step closer to the kind of highlighting and scribbling practices that we are used to with paper and pen.

Adding personal annotations is one thing, but different kinds of tools are needed to have an online discussion about a document. An example of a system designed to enable web document discussion is The Open University's Digital Document Discourse Environment (D³E; see Screen 6, below). A toolkit converts a standard web document into the environment shown, making it easy to navigate online between sections, follow references and comment on or discuss with others any specific section or theme in the document.

A step further is The Open University's experimental ClaiMaker tool, which allows a group to highlight ideas in a document, ‘tag’ them as a significant contribution of some sort (for example, Evidence, Prediction, Theory) and then link them to other such concepts (for example, proves; is inconsistent with; is analogous to). A network thus emerges from the collective views of the group's members, some of which may be contradictory. Analysts can challenge each others’ contributions if they wish, so that principled arguments can be mediated. This network can be visualised and searched in novel ways; for example, Show me all documents reporting evidence which challenges this prediction. This is an impossible question to ask with today's search engines, since they have no model of meaningful structures at this level of abstraction, operating instead at a lower level of semantics such as clusters of terms, or, at most, the ability to spot that one document cites another. Recent research indicates the possibility of automatically inferring the semantics of connections between documents within a specific field, but this is still very much in the research laboratories.

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Screen 6

Screen 6, above, shows how the D³E system (d3e.sourceforge.net) generates a document-discussion interface from a conventional HTML document (as used for document review in a journal). On the left is the Article Window, on the right the Commentaries Window showing the top level outline view of discussion about the document.

Key: 1. Comment icon embedded in each section heading: clicking displays section-specific comments; 2. active contents list extracted from the section headings; 3. print versions as HTML and PDF; 4. numeric or author/date citation automatically linked to corresponding reference in footnote window; 5. a reverse hyperlink is inserted for each citation of a reference; 6. an editorial note to draw attention to a controversial issue in the author-reviewer debate that ‘made it’ into the published version; 7. section-specific review comment; 8. an editorial comment summarising the review discussion and specifying change requirements. (Note that there are two versions of the user interface: one as shown, and, for smaller displays, the document and discussion are placed in separate browser windows)

Box 4.11 Capturing group memory as a hypertextual web of concepts

‘Wicked problems’ (Rittel and Webber, 1973) have a number of characteristics that you will no doubt recognise from your own work. Such problems:

cannot be easily defined, making it difficult for all stakeholders to agree on the problem to solve
require complex judgements about the level of abstraction at which to define the problem
have no clear stopping rules (usually when resources run out)
have better or worse solutions, not right and wrong ones
have no objective measure of success
have no given alternative solutions – these must be discovered
often have strong moral, political or professional dimensions (you may lose your job if you get it wrong).

These are the typical challenges faced in strategic planning, upstream design and government or social policy formulation.

Knowledge management tool requirements. Knowledge technologies to assist in the analysis of such problems have to take seriously a number of issues that have been emphasised in this unit: the complexities of trying to bring together diverse communities of practice (negotiating different agendas and language), poorly understood domains (making concepts hard to organise) and tacit factors that again may be hard to represent (for example, decision criteria that may range from technical to political in nature).

Compendium. One promising approach is the Compendium methodology and suite of tools. This has emerged from over a decade's joint research and development at the intersection of collaborative modelling, organisational memory, computer-supported argumentation and meeting facilitation. It takes as its starting point the face-to-face meeting, which is the most pervasive knowledge-based activity in working life, but also one of the hardest to do well. Compendium's developers report that the combination of facilitation with visual hypertext tools can improve potentially unproductive or explosive meetings between multiple stakeholders with competing priorities (Selvin et al., 2001). Diverse perspectives can be captured, structured and integrated in a way that all participants collectively own as a trace of their discussions. In the process this constructs a structured, group memory which shows where the same concepts have been discussed in different contexts, why decisions were made, and allows one to harvest related concepts from multiple meetings (see Screen 2 for an example from Box 4.4: NASA collaboration tools).

Integration with other tools and work processes. These ‘conversational maps’ often need to be integrated with pre/post-meeting activities and documents. For instance, written documents can be converted into concept maps, so that their contents can be analysed in new ways and integrated with other maps. Conversely, organisational documents (conforming to the requirements and expectations of other stakeholders) can be generated directly from concept maps.

For details of this approach and a business case study, see below for the article entitled ‘Rapid knowledge construction: a case study in corporate planning using collaborative hypermedia’ by A. Selvin and S. Buckingham Shum.

View document

Long description

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4.12.1 Communities of practice and technology

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Communities of practice are technical and social networks which set the context in which new knowledge arises in daily work, and determine how it is shared and interpreted, what counts as important knowledge and how people become recognised as members of that community:

A good deal of new technology attends primarily to individuals and the explicit information that passes between them. To support the flow of knowledge, within or between communities and organizations, this focus must expand to encompass communities and the full richness of communication. Successful devices such as the telephone and the fax, like the book and newspaper before them, spread rapidly not simply because they carried information to individuals, but because they were easily embedded in communities.

(Brown and Duguid, 1998, p. 105)

Brown and Duguid (1998) make certain suggestions concerning the way in which the community of practice might shape how we implement technologies to foster knowledge creation, sharing and management. These are paraphrased and elaborated upon in Box 4.12.

Box 4.12 Some technology implications of focusing on the community of practice as the unit of analysis

Technologies should permit multiple degrees of formality in communication: communication with trusted colleagues and within a community of practice is more informal and can assume more common ground than communication between communities of practice. A technology such as email or conferencing will be intrusive, and may be rejected, if it enforces the explicit negotiation and encoding of roles, responsibilities, obligations, permissions, and so forth, that are normally tacitly negotiated. The cognitive demands of encoding such meta-information may also be too high.

Technologies should permit peripheral participation in online forums: studies of communities of practice identified a pervasive phenomenon in situated learning, namely that newcomers value being able to ‘lurk’ (to use an internet term) on the periphery of a community of practice, whether a physical one in the workplace or in an online forum (such as an email listserver or a Lotus Notes discussion), so that they can learn from more experienced people and gauge the level and tone of debate. Gradually, they move from the periphery toward the centre, as they participate, and take on responsibilities within the community of practice. Lave and Wenger (1991) termed this ‘legitimate peripheral participation’. The implication for technologies is that exclusion of staff from digital resources, particularly social, communications-based resources, can impede this form of apprenticeship.

The value of digital boundary objects should be recognised: a ‘boundary object’ is an object or representation of interest to two or more communities of practice. They both have a stake in it, but from different perspectives. A boundary object might be a technology (for example, how a new online document archive should be structured), a technique or method (for example, how customers should be consulted, how software should be designed), or a document (for example, what should go into an organisational policy). It might even be a place or an object (for example, where a new laboratory should be sited). Effective boundary objects provide mutually comprehensible starting points for discussion, encouraging each community of practice to explain its rationale. Boundary objects are therefore devices which can foster communication between communities and, if well designed, make it possible for geographically dispersed communities of practice to focus online debate around a particular object. (See again Screen 6 for an example of a web environment for document discussion.)

To summarise, all the technologies for tacit knowledge discussed in this section have a focus on the social fabric and informal communication that underpins a community of practice. The emphasis is on augmenting communication by mediating and hence structuring it electronically, and/or by adding functionality to digital artefacts to allow their meaning to be negotiated explicitly. In Section 4.3, we look at knowledge-based systems which focus on types of knowledge whose structure can be codified at a finer granularity in order to make forms of ‘machine reasoning’ possible.

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4.13 Technologies and explicit knowledge

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Knowledge-based systems have the ability to analyse specific kinds of information in order to take action. Since we have earlier defined knowledge as arising out of the interpretation of information as mediated by representations, we can claim that in a limited sense such systems can ‘know’ things: they have a representation of part of the world, and they have some rules that allow them to analyse that representation, from which they can decide on a course of action. In that sense, they have interpreted information in order to reach a conclusion, or, as some authors express it, they have ‘reasoned about’ or ‘made inferences about’ the information to derive new knowledge. In this section we will look at what is meant by a knowledge-based system, and how knowledge can be represented formally and ‘reasoned’ about by the system. But first, a brief comment on standardisation.

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4.13.1 Standards and classification

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

ICTs depend on myriad standards in order to provide interconnectivity. If this was a computer science course, you would be learning about standard network protocols which enable computers to communicate with each other or with other devices, whether over the internet or from your computer to a network printer. Standards enable us to send email and browse websites without worrying about the underlying mechanisms (until they fail, forcing us to focus on the tool instead of our work).

We are concerned here with organisational knowledge and technologies. Our concerns are less with low-level data protocols than with the interchange of standardised (at least within a team or organisation, if not internationally) knowledge-level categories of information between computers. By ‘knowledge-level’ we refer to ways of encoding information into categories which are recognised by computers, but also are meaningful to people who are trying to make sense of the information.

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4.13.2 Example: an ‘intelligent’ email system

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Let us work through an email example of making a system ‘smarter’. We are all familiar with the standardised fields in an email system: From, To, Subject. The computer needs the To/From information, expressed in a standard format, to direct the message to its addressees and allow them to reply. It has no concept of who the sender and recipient are, or what the Subject field means. We can imagine simple knowledge-level email categories which add status information to these messages which is meaningful to a human, such as needs action by end of today, or highly reliable source. These could be menu items or checkboxes that the senders could select on their message. Again, while the sending/receiving computers have no model of what these mean, if they are recognised as ‘official’ categories then some automatic action can be taken at the receiver's end (for example, highlight in red, copy to group manager, print immediately). More advanced email systems in fact allow users to create filters that can take such actions if the content of messages matches user-defined criteria.

What would be required to give the computer more ‘understanding’ of the message's Subject, or about the To/From fields? Suppose we wanted a computer system to automatically update its internal model of the world if Sue sent a message saying that she had just passed a course. It might even infer that IF Sue has completed Course X, THEN she must know about A, B and C. It might then initiate some consequent action: for example, since Sue now knows about B, inform Fred who manages B-knowledgeable staff. We need to provide the computer with a model (a representation) of important concepts, and rules about what to do given certain states in that model. Three approaches are outlined next.

Users structure more information

One approach to building such a knowledge-based email system would be to allow users to move beyond the standard information fields in an email message, and add additional information fields: for example, in a staff development context, notification of new qualification might be a significant event worth formalising. The computer would also need a model of courses (for example, Course X has modules A, B and C) and staff (for example, staff study courses; Sue has studied Course X; Fred manages B-knowledgeable staff; Fred's email address is fred@blah.com). Now, when Sue sends this message, the computer system can detect this significant event, update its own model, and perhaps take consequent action.

Computer infers information from unstructured information

A more advanced system might not require a predefined field to detect notification of new qualification, but might directly analyse the unstructured text of Sue's message. It might use natural language processing, the field of computer interpretation of spoken and written human language. Related techniques do not seek full natural language processing, but try in a more limited manner to extract key concepts and relationships from unstructured text (sometimes called ‘text mining’ or ‘information extraction’). A computer with such abilities might be able to analyse the text in the Subject or body of an email, build a model of key concepts and relationships, and take some action on this basis. In addition to the requirements in the first example above, it would need a model of how the written language is constructed (for example, English grammar, or a subset of it specific to the field's subject matter) in order to isolate key concepts and relations.

Computer helps users structure information

A third approach falls in between the above two in terms of how much responsibility for interpretation is assigned to people or computers. The approach of incremental formalisation (Shipman and McCall, 1994) can help address the situation in which a knowledge base already exists within an organisation, but documents have not been encoded using this scheme. The system can search documents and messages for keywords that match important concepts that it ‘knows about’ (for example, Course X), and try to ‘fill in the form’ that would submit a new entry to that repository (for example, a form for updating Sue's professional qualifications). Sue would be presented with this form, completed as far as the system had managed (for example, inserting the name of the staff member and the course), she would check its accuracy, and have less work to do to complete the remaining fields that the system had not managed to infer (for example, her grade).

Incremental formalisation can also assist in building the ontology itself (the scheme underpinning the knowledge base) by suggesting new classes and structures. We examine more closely the concept of ontologies for modelling knowledge after briefly reviewing ‘metadata’ below.

Box 4.13 So, what can a computer ‘know’?

Although a computer can represent a relationship such as staff study courses, it has no notion of what this really means in the real world. It simply knows about an entity labelled staff, which has instances such as Sue and Fred who have email addresses, and that study is a legitimate labelled relationship between staff and courses.

We could replace staff study courses with nonsense expressions such as frogs like fudge, or bloot murve doinkies, but to the computer these terms are just as legitimate and ‘meaningful’ if they are substituted consistently throughout its model!

The power of the computer derives from its ability to store and track large networks of interdependences that would be taxing for humans to do – perhaps impossible cognitively – thus inferring new states in the model, taking action where rules have been defined, helping maintain the repository, perhaps suggesting relevant material of interest based on inferences about conceptual relationships, and communicating with other computers that also know about these concepts.

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4.14.1 Metadata

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Metadata is descriptive data about data. This has also come to refer to a way of tagging documents (on the Web or any other repository) with structured, descriptive information. For example, to describe a unit in B823, we would expect to have concepts such as title and author, but perhaps also prerequisite or core concepts. Translated into a metadata scheme, this might appear as follows (typically metadata fields use <angle brackets> to delimit each metadata tag).

Note that some of the metadata tags simply describe the content of the unit, while the last two actually describe particular kinds of relationships to other units. Unit 7 is not a PREREQUISITE-FOR B823-Unit-3, it BUILDS-ON it. This is specialist knowledge supplied by the unit authors: it may not be explicitly in the content of the unit's text. Metadata schemes are designed to be machine interpretable, like ontologies, but also to be accessible to people without extensive training. You can see that the scheme above looks more like a series of structured keywords. Often, the average member of staff would not even have to see this cryptic notation since it would be entered and displayed using an online form.

Information such as this enables much more powerful searching than is otherwise possible, once a search engine has been given the description of the particular metadata scheme (that it uses ‘tags’ for title, course, prerequisites, etc.). The search engine can then provide a query form with fields for these tags, and users can search specifically for course units which, say, have ‘interpretation’ as a CORE-CONCEPT, or all units that BUILD-ON B823-Unit-3 and are a PREREQUISITE-FOR B823-COMPLETION. This is impossible to do without the equivalent of structured metadata, but is still the state in which much of the Web exists – we have to simply search using keyword combinations, and then manually filter out a lot of irrelevant documents returned by the search engine. By thinking carefully about the metadata scheme's design (that is, by designing an ontology of the domain), we can transform a mass of documents into ‘knowledge-enriched’ materials whose content and interrelationships can be accessed without having to read every document.

The terms which one chooses to encode in a metadata scheme are typically derived from an analysis of the field, and the kinds of search queries that one anticipates supporting. They may be derived from an organisational or community taxonomy (hierarchy) of terms, or a library indexing scheme. One can, however, go one step further and formally model the relationships between terms such that a computer can reason in more sophisticated ways about the relationships between terms. If we declared that an <AUTHOR> IS-A <SCHOLAR> who PUBLISHES <ARTICLES> and IS-AFFILIATED-TO an <INSTITUTION>, then we are creating a richer model, which is called an ontology.

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4.15.1 Ontologies

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

We noted earlier that, in philosophy, an ontology refers fundamentally to ‘being’, or ‘what can be’. In the field of artificial intelligence the term ‘ontology’ has been appropriated to mean a ‘reusable terminological scheme’ or, if you prefer, a ‘conceptualisation’: a scheme for providing a rigorous description of the concepts, attributes and interrelationships deemed relevant to describe a particular aspect of the world. Its precision means that it can serve as an agreed model of a domain to ensure a common point of reference between parties. An ontology is an abstract knowledge model in the sense that, like an agreed standard, it does not need software in order to exist (it is a specification of how to talk about the world). However, a strength is that, if the ontology is created in digital form, software tools can assist in checking its internal consistency, and can convert it into a knowledge-based system for a particular application to a problem.

Consider the example shown in Figure 10, taken from a healthcare support system.

If someone has already invested much effort in deliberating about the ontological structure of a particular domain, others who are seeking an understanding of that domain could benefit from this, rather than having to reinvent the wheel. Numerous knowledge-sharing initiatives are under way, developing notations for expressing ontologies, which are then published and exchanged over the Web. A common notation makes it possible to exchange ontological structures that can then be embedded into someone's own ontology, if they are using the same notation. (In reality, variations in the use of language and approach usually entail some tailoring to make someone else's ontology ‘plug into’ your own.)

(Source: HC-REMA, 1997)

View larger image

Figure 10 Graphical representation of an ontology specifying what ‘economic pay-off’ means in the context of other healthcare concepts

Long description

Part of the notational specification (using the Ontolingua notation: Gruber, 1993) for the graphical ontology structure in Figure 19 is shown in Box 4.14. It is immediately obvious that only specialists will wish to read and write such specifications. The notation is formal to enable automatic generation and interpretation of ontologies by computers, but at the same time knowledge engineers can read and write such code. The services (such as ‘smart email’) enabled by having a partial model of the world would then be delivered to end-users using conventional user interfaces.

Box 4.14 Part of the specification for the graphical ontology structure in Figure 10

In principle, we would expect notations and tools for modelling the structure of specialist knowledge to have great potential for knowledge management. At present, however, knowledge modelling is still largely in the research laboratories. The effort and expertise required has meant that few ontologically based technologies have been widely used on a significant scale. A critical factor that will determine whether ontologically ‘enriched’ technologies are adopted is the usability of the system. How much understanding of the ontology is required to use it effectively? Can new entries be added easily to the knowledge base at the right moment? We may see technology vendors beginning to embed more explicit representations of knowledge in their systems in the next few years, particularly given the high profile that two related technological developments are receiving: metadata, as discussed above, and software agents, which are discussed later in this section.

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4.16.1 Ontologies + the Web = the Semantic Web

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Tim Berners-Lee, the inventor of the World Wide Web, has defined a vision of the Web's evolution into the Semantic Web:

The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. The first steps in weaving the Semantic Web into the structure of the existing Web are already under way. In the near future, these developments will usher in significant new functionality as machines become much better able to process and ‘understand’ the data that they merely display at present.

(Berners-Lee et al., 2001)

The key idea is that, while Web pages started out being designed by and for humans, there are situations where they could be usefully interpreted by computers, if there were structured descriptions of them using ontology-based metadata. The HyperText Markup Language (HTML) is the simple mark-up scheme on which much of the World Wide Web is based to date, for human interpretation. The Extensible Markup Language (XML), coordinated by the World Wide Web Consortium, makes it easy for an organisation to define new kinds of tags and document types that reflect its concerns. This unit, for example, is rendered in HTML and is also available as an XML file to download in the LabSpace. An ontology web language such as OWL constrains the XML markup terms so that their meaning can be controlled, and some forms of automated reasoning can be done.

Computers will be able to read formally codified information attached to web pages (for example, in XML format) to reason and search. There is now an enormous amount of activity to realise this vision, both in business (there are many e-business and knowledge management applications) and research (for example, a further step towards a global library). Ontologies, as introduced above, offer one way to automatically resolve ambiguities about the meaning of such formally codified information – there can now be a pointer to the ontology from which a resource's vocabulary is taken. Software agents (introduced below) are programs that will be better enabled by ontologies to collect, analyse and share information semi-autonomously.

An important distinction to grasp is that there will not be a single Semantic Web, analogous to the Web we have today. Analogous to the ‘islands of local coherence’, referred to when we discussed mapping knowledge across communities of practice, there will be many Semantic Webs, each one being a set of websites and ontologies which its users trust, and have built systems to use.

Activity 4.3

How does the use of formal ontologies relate to perspectives such as situated action, tacit knowledge, communities of practice and boundary objects? Is there a contradiction in embedding codified knowledge models in systems (which seek to systemise and control meaning), while also subscribing to social, situated conceptions of knowledge (which emphasise that meaning is context dependent)?

Discussion

Ontologies are used to control interpretation, to avoid misunderstandings and confusions. This is fine for machine-machine communication (for example, software agents), but enforcing their use for communication between people can, of course, be harder. Language is a slippery thing. Effective use of a controlled vocabulary is more likely to be possible within a community of practice, since the ontology's terminology and perspective may not correspond to the interests, perspectives or concerns of other communities of practice. Another way of putting this is that ontologies require consensus on what should be included and how it should be structured.

Ontologies (and the knowledge-based systems which implement them) are expensive to design in terms of intellectual and development effort, and so are more cost-effective for representing stable aspects of the world that are unlikely to radically change their structure.

While an operational knowledge-based system can only be used in a well-understood domain, the process of trying to construct informal, ‘disposable’ ontologies can be illuminating. Rough outlines and concept maps focus attention on what counts as an important distinction. If different communities of practice are involved, these can therefore serve as a boundary object to uncover assumptions.

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4.17.1 Software agents

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

A software agent is a program that displays a certain minimum level of autonomy – it acts as a surrogate for a human user. An agent does something for the user automatically, when given instructions. The more sophisticated the agent is, the fewer instructions it needs, and the more capable it is of making decisions on its own – the more ‘agent-like’ it is. An agent can be run on a client (the user's machine) or on a server (for example, a web server). It can also be anchored (stationary on a machine) or mobile (moving itself from one machine to another), depending on the underlying technology.

Software agents can be defined as: ‘a software entity which functions continuously and autonomously in a particular environment, often inhabited by other agents and processes’ (Shoham, 1997, p. 7). Agents are sometimes, perhaps contentiously, called ‘intelligent agents’. This reflects capabilities such as:

learning algorithms, enabling agents to adapt themselves to user routines and preferences
planning algorithms, enabling agents to construct a sequence of steps to accomplish a goal
negotiating protocols, which make it possible for agents to reach agreements on sharing resources, for instance on buying and selling.

The following quotation explores the characteristics we might expect from an intelligent agent:

The requirement for continuity and autonomy derives from our desire that an agent be able to carry out activities in a flexible and intelligent manner that is responsive to changes in the environment without requiring constant human guidance or intervention. Ideally, an agent that functions continuously in an environment over a long period of time would be able to learn from its experience. In addition, we expect an agent that inhabits an environment with other agents and processes to be able to communicate and cooperate with them, and perhaps move from place to place in doing so.

(Bradshaw, 1997, p. 7)

Agent research is a rapidly growing field, spawning many new software companies, and agents are finding extensive application in the Web as aids to finding material of interest in the ocean of information. In a knowledge management context, agents are proving themselves in different ways, some of which are summarised below.

Seeking out information which the agent thinks will be of interest: for example, InfoFinder learns a user's interests by analysing sets of messages and other online documents that the user classifies (Krulwich and Burkey, 1997). Another example is the Remembrance Agent (Rhodes and Starner, 1996), which displays a list of documents that might be relevant to the user's current context. Recreational agent systems are available to help internet users find friends and music which the agent thinks they will like, but the underlying technologies are applicable for knowledge management problems such as locating experts (for example, MIT Media Lab, agents. http://agents.media.mit.edu/ projects.html).
Acting as a ‘virtual participant’ in a discussion: for example, the Virtual Participant agent (Masterton, 1997) which ‘listens in’ on FirstClass electronic conferences, builds summaries of them and tries to retrieve relevant past discussions in response to questions.
Managing the low-level communication and formatting of data from multiple databases when a user issues a search: for example, a network of online information resources which may vary widely in format and organisation. The end-user only cares about finding relevant material. A set of cooperating agents can be used to handle the complexity of the underlying infrastructure, requiring only one user interface, as if querying a single, static database whose terms and relations reflect the user's perspective of the knowledge domain.

Very basic agents are already being embedded in products – for example, the Wizards in Microsoft's Office suite – to provide contextualised help and hints. Possible future developments in the field include easier ways for the user to program and fine-tune their instructions, agents with deeper knowledge of specific areas and agents that can cooperate in more sophisticated ways.

Ontology-based software agents

Ontology-based software agents use ontologies to provide a lingua franca for agents to communicate within a given field. The analogy here recalls our earlier discussions about the importance of shared background knowledge in interpretation – without some common level of familiarity with a field, it is very hard to discuss it. Similarly, if computers need to query or share their knowledge, they need some common terminology. This is precisely the purpose of an ontology, providing a basis for defining concepts and their relationships. Returning to our email example, another computer in the organisation that wishes to find staff trained in Course X will be able to find Sue if its ontology shares these concepts.

Software agents are being acclaimed in some quarters as providing the basis for electronic commerce over the internet (for example, Ontology.Org, www.ontology.org). If agents shared sufficient ontological knowledge, it is possible to imagine them being commissioned to locate and negotiate with other agents.

We now consider techniques which seek to identify patterns in databases without requiring the prior structuring of categories that ontologies assume.

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4.18.1 Data mining

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Data mining refers to techniques for analysing databases or information systems to try to identify hidden but significant patterns that are not possible to detect by standard querying of the database.

Moxon defines data mining as follows:

Data mining is a set of techniques used in an automated approach to exhaustively explore and bring to the surface complex relationships in very large datasets … most likely implemented in relational database management technology. However, these techniques can be, have been, and will be applied to other data representations, including spatial data domains, text-based domains, and multimedia (image) domains.
Data mining … uses discovery-based approaches in which pattern-matching and other algorithms are employed to determine the key relationships in the data. Data mining algorithms can look at numerous multidimensional data relationships concurrently, highlighting those that are dominant or exceptional.

(Moxon, 1996)

Data mining techniques seek patterns such as associations, sequences and clusters in databases. Ideally, these should have predictive power to enable informed planning and decision making. To call the output from such tools ‘knowledge discovery’ is a little over-inflated. We have already seen that what counts as useful knowledge will depend on the human's interpretation of the information presented: are the associations, sequences and clusters meaningful and significant with respect to the task at hand?

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4.18.2 Information visualisation

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

We read increasingly of the problem of information overload. Earlier, we emphasised the importance of designing appropriate information representations to assist human interpretation in order to create actionable knowledge. Information visualisation is concerned explicitly with designing representations using intuitive visual metaphors and graphics to highlight the most important aspects of information structures and processes. Information visualisation is a rapidly emerging area which will grow in importance as the information ocean continues to swell, and as high-performance desktop computers and networks plummet in price, making sophisticated three-dimensional graphics affordable. Information visualisation seeks to design visual ways to communicate large amounts of information in intuitive ways, with particular emphasis on using visual cues to augment text. A useful resource on this is provided by Chen (1999).

An important myth to dispel is that visual languages are intrinsically superior to text. The literature of those marketing (and even researching) information visualisation is often filled with hyperbole, typically arguing that since the human mind can absorb and process vast quantities of visuospatial information, visual representations (of the Web, documents, system or user behaviour over time) are the key to the future. More careful analysis by cognitive scientists clarifies that:

There are very few useful ‘pure’ visual representations – they are usually graphical/textual hybrids.
Often text can be formatted far more clearly than it actually is, introducing visuospatial attributes (for example, using indentation to show structure, or font formatting to highlight key concepts).
The efficacy of visualisations is intimately linked to the task that the particular kind of user has to perform. Visualisations can undoubtedly communicate more information, and reduce cognitive load in certain circumstances, but they can equally hide information and increase cognitive load in others.

You will no doubt have seen on television computer-based models of chemical structures being rotated, perhaps using virtual reality to ‘immerse’ the viewer in the world they are exploring. But what might it mean to render corporate knowledge like this? Or to detect trends in the global markets as though they were oceans with complex currents and eddies? Information does not often have an obvious ‘structure’, like a molecule or atom; it has many possible structures, depending on our perspective. How can this be made tangible?

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4.19 Technologies and explicit knowledge continued

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

The following examples give a taste of what is now making the transition from research laboratories into commercial products. Large hierarchical information structures are extremely common, whether in libraries, organisational charts or websites. Displaying such large structures is a challenge, and since the user soon runs out of screen space, navigating them can be tedious. Screen 7 shows a system that uses animation and carefully designed graphical effects to give the impression of manipulating a three-dimensional structure floating in space. Experiments provide evidence that this kind of interface enables users to browse and search large hierarchies such as library indexing schemes, organisational charts and websites more quickly than using traditional two-dimensional interfaces.

View larger image

Screen 7 The ‘Cat-a-Cones’ interface (Hearst, 1997), which uses three-dimensional ‘cone trees’ (in the background) to display large hierarchies (for example, the structure of a large website). Clicking on one of the nodes in these trees displays that page in detail (the ‘book’ pages at the front). The results of previous searches are stored in ‘books’ on the shelves to the left

Hierarchies are easy structures to understand. However, other kinds of information have no obvious visual corollary; for example, how does one visualise a library of documents? A large organisation may have hundreds of reports, which typically will be organised only in conventional ways, such as author, date published, project and keywords, and which may be searchable if they are online. However, there are many conceptual links between documents that only become apparent on reading them. Can any of these be automatically detected? Box 4.15 summarises one approach.

Box 4.15 Technology briefing: mapping hidden conceptual structures in digital libraries

Information retrieval techniques are emerging which analyse libraries of digital documents using statistical analyses of terminological frequency and relatedness in order to identify potentially significant clusters of documents. For example, Chen (1999) describes techniques for visualising conceptually related document clusters as maps which can then be delivered over the Web. Such automatic indexing techniques are ideal for large corpora of documents that have no metadata or other form of classification information. Screen 8 is an example generated from a research approach called ‘generalised similarity analysis’. Generalised similarity analysis seeks to identify conceptual structures in a digital library by combining one or more measures: latent semantic indexing (statistical measures of similarity based on keywords in documents), hypertext navigational patterns (reflecting the most heavily navigated routes) and connectedness (for example, by citation or hypertext link). Next, these structures are then visualised using an automatic map layout algorithm, which spatially clusters apparently related documents into branches. Finally, these are delivered over the Web as interactive maps that can be browsed and searched as virtual reality models (using the Virtual Reality Modelling Language).

In the longer term this promises a way to generate automatically visual overviews showing different perspectives of a domain.

View larger image

Screen 8 Using automatic techniques to map conceptual structure in a digital library, accessed over the Web (for examples such as this see Chen, 1999). In the left frame is an automatically generated map of a collection of digital documents (spherical nodes), clustered according to similarity. The structure can be viewed from any angle and distance, and clicking on a node displays the associated document (right frame)

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4.20 Technologies and explicit knowledge continued

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In the future we will see the fusion of statistical analyses of documents, agents, ontologies, metadata and informal annotation/discussion. Ontological tagging with metadata would allow authors to express their own deep understanding of the domain which may draw on knowledge that is not in the text of documents. This would allow experts to set a document in context in the light of developments since the document was written, or to encode relationships between documents that show important connections (cf. ClaiMaker in box 4.10). An organisational taxonomy can also be used to map relationships between documents and specific problems/processes of concern to the organisation (for example, ‘this report describes a solution to problem X’). Autonomy markets text mining software agents with the claimed ability to extract the key concepts from any document on the web or an intranet.

Box 4.16 Technology update: sonification and audio spaces

We are constantly absorbing and processing multiple sources of auditory information, both in everyday and work contexts, without even thinking about it. So why not use sound, as well as visual techniques, to communicate complex information?

In the workplace we can use the sound of people gathering to determine when to join a meeting, or the particular whirr of a computer's disk drive to tell if a program is launching properly. We can process this information in parallel because of the modality difference: the types of information which are suitable for aural presentation are quite different from those presented visually. Sound is temporal and three-dimensional in nature, and this opens up the possibility of presenting multiple streams of time-varying information to the user simultaneously without cluttering the visual channel of communication.

Researchers are now exploring ways to integrate the two or replace visual cues with audio cues, particularly for visually impaired users. However, everyone should benefit from this research. As multimedia PCs become the standard, auditory cues are beginning to appear on our desktops to provide feedback on background processes that we do not want to visually monitor (for example, printing, copying or the arrival of email).

In the future we may see developments such as the following:

three-dimensional audio spaces which simulate (through speakers or headphones) sounds coming from different positions, making it possible to process more complex information
analysis of the performance of software by listening to ‘sonic signatures’ that can highlight errors (simulating digitally a common practice of the programmers of early valve computers!)
presenting complex, multidimensional data sets in sound, enabling patterns and trends in the data to be heard in a holistic fashion
the use of high-quality synthesised speech and non-speech sounds to enhance navigation around graphical user interfaces, for both sighted and visually impaired users new paradigms for visually impaired computer users which replace the paper-based metaphor with more appropriate metaphors for the audio dimension
audio-based web browsers.

Activity 4.4

Having now completed this unit, read the article ‘Rapid knowledge construction: a case study in corporate planning using collaborative hypermedia’ by Albert Selvin and Simon Buckingham Shum.

View document

As you read, think about the following questions:

How does the ‘Rapid Knowledge Construction’ (RKC) approach position itself with respect to a key challenge highlighted in this unit: namely, the process of ‘formalising knowledge’?
How does the approach seek to address the needs of different communities of practice?
If asked to give an assessment of Compendium's potential for your situation, what would you say?

Discussion

Compendium, as an example of RKC, is a semiformal approach to structuring knowledge. It is more formal than simply writing down ideas on a flipchart or slide, through the use of a notation (Questions, Ideas, Arguments) which are mapped as iconic nodes and linked to create trees and networks. But it is less formal than an expert system knowledge base designed for fully automated processing, since the labels and contents of nodes in the map can be anything. This ‘relaxes’ the constraints on what can be mapped, which is critical for a tool designed to be used during meetings, and, especially, to capture the perspectives and arguments between diverse stakeholders. Consequently, the strategy for tackling the ‘knowledge capture problem’ is to add a degree of structure which benefits both the people (clearer structure to ideas and meetings; immediate validation of what is going into the group memory) and the computer (which can process the structures to a degree without understanding anything about the content of nodes).

Different communities of practice are supported (a) by making it possible for a group to meet together and work on constructing an agreed definition of the key issues, options and trade-offs, using a simple notation which all can understand; and (b) because in this case study, ‘Question’ templates were used to drive the discussions, and the software was able to process these regular structures and transform them into the required organisational documents. Since Compendium uses a fundamentally dialogic approach, it scaffolds the process of making and taking perspectives, and offers visual boundary objects for groups to agree on.

Although not discussed at length in the article (but exemplified in the NASA collaboration tools case study – Box 4.4), an additional way in which Compendium recognises the needs of different communities of practice is that they each have their own specialist tools, and, indeed, not all can be expected to simply start using Compendium. A key function of a tool to support sensemaking is that it can work smoothly with an organisation's existing ICT infrastructure, providing conceptual and technical ‘glue’ between otherwise disconnected ideas, documents and data. Compendium has been engineered to make it technically open’ so that it can import and export data to other systems.

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5 Conclusion

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Knowledge technologies, as software systems, embody formal models of how the world works: for example, networks between people, what their roles are, how information should flow, rules about interdependences between variables, and how to index and categorise information. If well designed, such models relieve people of mundane activities, allowing them to focus on what they do best: communication, negotiation, creative problem solving: that is, the construction of new shared meaning. At their best, knowledge technologies can detect patterns in information which are too complex for humans to detect, or which they do not have time to detect, and can deliver this information to the right people, at the right time, in the right form for interpretation.

Revisiting the core themes introduced at the start of this unit, there are philosophical representational reasons why these models can never fully mirror the complexity of the living, social worlds that humans construct for themselves in organisations. Representation is always selective. Mapping is always a question of how to distort the world in order to serve a particular perspective. The codification process distances the knower from what they know tacitly, enabling them to symbolically represent and manipulate ideas as objects, which can then be embedded into programs and computational models. This process, however, necessarily changes the nature of the knowledge through abstraction and decontextualisation. It is critical, therefore, that the users who, after all, create the meaning and significance from the data and information, are fully involved in evaluating the design of knowledge management technologies from the start, to ensure that the formalisation process of software design does not destroy the very ecology it seeks to nurture.

The core concepts of representation, interpretation, situated use in context and communities of practice draw attention to the ways in which such tools are subsequently integrated into the cognitive, social and organisational flow of work. We can never fully predict the use of a design because we can never fully predict how artefacts will be interpreted and appropriated into working life. New technologies trigger changes in the ecology of work, which adapts to try to incorporate the technologies into work practice. In the worst case, no ecological niche can be found and the system is rejected or worked around. In the best case, the ecosystem functions more effectively as a result of mediating new activities technologically.

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WEBSITES

Access Grid video conferencing/collaborative workspace tool: www.accessgrid.org

Autonomy text mining technology: www.autonomy.com

BuddySpace enhanced team presence and messaging: www.buddyspace.org

ClaiMaker semantic annotation of documents: http://claimaker.open.ac.uk

Compendium concept mapping for collaborative analysis and meeting capture: www.CompendiumInstitute.org

D³E: Digital Document Discourse Environment: www.d3e.sourceforge.net

Dynamic Diagrams: www.dynamicdiagrams.com

eClass meeting capture tools: www.cc.gatech.edu/ fce/ eclass

Eureka professional story sharing system, Palo Alto Research Center (PARC): www.parc.com/ research/ spl/ projects/ commknowledge/ eureka.html

Flashmeeting Web browser/Macromedia Flash video conferencing: www.flashmeeting.com

Hexagon video presence portal: http://cnm.open.ac.uk/ projects/ hexagon

Lotus Notes: www.lotus.com

MIT Media Lab (Software Agents Projects): http://agents.media.mit.edu/ projects.html

NASA ASK (Academy Sharing Knowledge) Magazine publishing Project Management Stories. Academy of Program and Project Leadership: http://appl.nasa.gov/ ask/

Ontology.Org: www.ontology.org

SMART Board: www.smartboard.co.uk

Stadium Webcasts, Knowledge Media Institute, The Open University: http://stadium.open.ac.uk/ podium

[All sites accessed December 2004]

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Acknowledgements

The Open University — Mon, 13 Jun 2011 14:45:30 GMT

Acknowledgements

Prepared for the Course Team by Simon Buckingham Shum

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Tables

Tables 3.1 and 3.2: Zimmerman, B. and Selvin, A. (1997) 'A framework for assessing group memory approaches for software design projects', Proceedings of ACM DIS ‘97: Conference on Designing Interactive Systems, ACM Press.

Figures

Figures 2.1 and 2.2 adapted from Stahl, G. (1993) 'Interpretation in design: the problem of tacit and explicit understanding in computer support for co-operative design', doctoral thesis, November, Department of Computer Science, University of Colorado;

Figure 3.1 Borghoff, U. and Pareschi, R. (eds) (1998) Information Technology for Knowledge Management, Springer-Verlag;

Figure 3.2 van Heijst, G., van der Spek, R. and Kruizinga, E. (1998) 'The lessons learned cycle' in Borghoff, U. and Pareschi, R. (eds) Information Technology for Knowledge Management, Springer-Verlag, pp. 17–34;

Figure 4.1 Jordan, B., Goldman, R. and Eichler, A. (1998) 'A technology for supporting knowledge work: the reptool' in Borghoff, U. and Pareschi, R. (eds) Information Technology for Knowledge Management, Springer-Verlag, pp. 79–97;

Figure 4.3 Repenning, A., Ioannidou, A. and Ambach, J. (1998) 'Learn to communicate and communicate to learn', Journal of Interactive Media in Education, Vol. 98, No. 7, http://www-jime.open.ac.uk/ 98/ 7;

Figure 4.4 HC-REMA (1997) HC-REMA: Health Care Resource Management, European Union Project HC4124.

Cartoons

Screenshots

Screen 8 Chen, C. (1999) Information Visualisation and Virtual Environments, London, Springer-Verlag.

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