KM Resources: Tools for Knowledge Management

This paper is also available in Microsoft Word format.

Anthony K.P. Wensley
Associate Professor of Information Systems
Executive Editor, Knowledge and Process Management
Joseph L. Rotman School of Management
University of Toronto
Toronto, Ontario, CANADA

Alison Verwijk-O'Sullivan
Research Associate

wensley@home.com

Introduction

I have deliberately drawn a wide net in the following discussion of tools for knowledge management. In the first part of the paper I could have restricted myself to only looking at information technology tools. However, although these tools are becoming increasingly important to many of the stages of knowledge management they are by no means the only tools available. I feel that it is important to investigate many of the issues surrounding the use of tools in knowledge management using a more general view of tools. This will afford me the opportunity of investigating the nature of knowledge management tools in general before focusing on some of the information technology tools that are available.

Another reason why I have chosen to first look at the nature of the tools that are available in the field of knowledge management is that there are a vast number of information technology tools available that can potentially support knowledge management and they are constantly being extended and augmented. Rather than attempt to create an exhaustive catalogue of these tools my research assistant has created a list of some of the Web-based information technology tools that can be used in knowledge management.

The following paper is divided into two distinct sections. In the first section I will discuss knowledge management and the nature of the tools that we may have at our disposal to manage knowledge. The second section of the paper presents an annotated list of some of the Web-based information technology tools that are available. As I have indicated above this list is necessarily incomplete but I hope that it will give the reader some grasp of the range of tools available. I would also encourage the reader to undertake a voyage through the many Web sites that are referenced. There is really nothing to replace first hand experience (?) of these tools. In the opinion of many the Web will, in the coming years, present us with a rich set of new tools for managing knowledge. As I indicate below this will primarily arise from two characteristics of the Web. First, it provides an extremely rich common language for representing knowledge - we have only just begun to explore the true nature of this richness. Second, it is an intensely interactive medium allowing for the sharing and cooperative development of knowledge. But more of these issues later.

It is interesting to note that much of the work in Web-based knowledge management tools derives considerable strength from what have been fairly mainstream research and applications in Artificial Intelligence. This relationship is likely to broaden and deepen in future years as I will indicate in more detail below.

Knowledge Management

Many researchers in the field of knowledge management seem to think that the field sprang into existence de novo a few years ago. This is demonstrated by a delightful attribution of the definition of knowledge as 'justified true belief' to Nonaka when, in fact, such a definition, though not in precisely the same words, was provided by Plato in the Socratic dialogues! Hubris of this order may be attributed to a number of factors. In the first place, knowledge management as a concept seems to have taken flight from the ashes of business process reengineering and a variety of other ideas first promulgated by management consulting firms. Newness and originality are often ascribed to old concepts on the belief that one can charge higher fees as a result! Second, information technology has given us data management, information management and now, logically, knowledge management. Information technology, from this perspective, created the opportunity for 'really' managing knowledge using information technology.

Having unfairly set up to straw men (persons?) let me set them aflame in good pagan fashion. Is knowledge management a new phenomenon? No. Although I certainly see ways in which knowledge management can be seen to have been born of such movements as business process management, customer orientation and the like, it is certainly not a new phenomenon. Further, with respect to the part that information technology has to play its existence is neither a necessary or sufficient condition for knowledge to be managed.

As I hope will be indicated in this paper information technology and the tools it provides can certainly support some aspects of knowledge management but knowledge management does not begin and end with information technology. I concede that data management probably does begin and end with information technology. In many ways information technology created the notion of data today. It allowed for the reduction of information into data and thus it would seem relatively uncontentious to argue that data management is only really possible with information technology. However, I certainly do not believe that this is true for information let alone knowledge.

Knowledge management has to do with the management of all stages in the generation, codification, refinement and transmission of knowledge. To the extent that I have any unique perspective in this area it is as a researcher who has been intimately involved in creating and codifying knowledge in specialist domains for at least a decade and a half. Although I am no longer directly involved in such creation and codification my research is now directed to many of the issues that arose during this decade and a half of prior research.

I have stated that the stages of knowledge management are generation, codification, refinement and transmission. What is involved in each of these stages? Ruggles (1997, p. 1) elaborates on the stage of knowledge generation as follows (p. 2)

"Knowledge generation includes all activities which bring to light knowledge which is "new," whether to the individual, to the group, or to the world. It includes activities such as creation, acquisition, synthesis, fusion, and adaptation."

Similarly, he expands on the concept of knowledge codification as follows (1997, p.2):

"Knowledge codification is the capture and representation of knowledge so that it can be re-used either by an individual or by an organization."

Different domains use different approaches to the codification of knowledge and knowledge workers in the same domain may adopt different approaches to codification at the same time. Codification may involve conceptual analysis, mathematical modeling and the development of restricted languages for structuring and communicating knowledge. I will have more to say about codification below but it is worth noting that Information Technology has provided some new twists to codification from efficient implementations of proofs in First Order Predicate Logic (FOPL) to a variety of data analysis and visualization tools.

Finally, Ruggles defines knowledge transfer (1997, p.2) as follows:

"Knowledge transfer involves the movement of knowledge from one location to another and its subsequent absorption."

When knowledge is transferred it is seldom transferred complete with all the details of its codification - indeed, it may be incoherent to state that knowledge could be transmitted so completely. In transferring knowledge there is an implied context - this context will relate to the way in which the knowledge was codified and how such codification should be interpreted.

Ruggles further notes (1997, p.2) that:

"Generation, codification, and transfer all occur constantly, so management itself does not create these actions. The power of knowledge management is in allowing organizations to explicitly enable and enhance the productivity of these activities and to leverage their value for the group as well as for the individual."

With respect to a definition of knowledge management tools (1997, p.3):

"Knowledge management tools are technologies, broadly defined, which enhance and enable knowledge generation, codification, and transfer."

Having created a very large canvas I will now proceed to examine a small portion of it. I will investigate how technology can be used to facilitate each stage of knowledge management. But first, I would like to talk a little about technologies and tools.

A distinction is often made between technologies and methodologies. I shall consider here that a technology is some human construct or artifact that potentially can enhance and enable human activities. Typically the way in which a technology is used is directed by some methodology - a set of ways of interacting with the technology. Thus, many impressive tools exist to assist in medical diagnosis, for instance MRI scanners. In and of themselves these tools are inert, they do not play any part in knowledge management. It is only when these tools are used in certain defined ways by communities of trained individuals who are able to communicate with each other that the can assist in knowledge management. In this context, a tool is one aspect of a technology that is typically used to achieve some specific purpose or related set of purposes.

Methods, Tools and Contexts

I think that it is appropriate to observe that mankind has, over the millennia, developed many different approaches to knowledge and knowledge management that have informed and been informed by both methodologies and technologies. These approaches are typically embedded in what Wittgenstein referred to as 'forms of life.' As Collins notes (1997, p. 148):

"If, so much knowledge rests upon agreements within forms of life, what is happening when knowledge is transferred via bits of paper or floppy disks? We know that much less is transferred this way than we once believed, but something is being encapsulated in symbols or we would not use them. How can it be that artifacts that do not share our forms of life can "have knowledge" and how can we share it? "

Clearly, one of the reasons that tools can support knowledge management is that they are embedded in particular ways of acting and value systems. Consider, for a moment, the Delphic Oracle. One could say that some of the tools for managing knowledge in this case were the women who made the oracular responses to questions. The women functioned as providers of knowledge partly through the implementation of a methodology concerning the interpretation of the oracular responses by the priests. Thus the tools gain their ability to be part of a knowledge management process through the use of methodologies that lead to the embedding of the tools in a particular 'form of life'.

When we make the popular distinction between tacit and explicit knowledge it is easy to forget that even explicit knowledge is only explicit because of a deep and richly understood context that allows us to interpret so-called explicit knowledge. This shared context is such a natural part of our forms of life that it is easy to ignore its existence until we discover/explore its richness to find that there can be alternative interpretations of what constitutes knowledge and understanding.

My central point here is that no knowledge management tool stands alone. It can only be understood in the context in which it is used and the methodologies that are associated with it. If we focus too much on the tools of knowledge management we may blind ourselves to this richness. So-called knowledge management tools can potentially be used to manage superstition and falsehood when used in inappropriate contexts.

There is also a danger that, in forgetting the role played by context, that we fail to grasp alternative perspectives. We no longer perceive the foreground as being intimately related to an arising from the background - we only see the foreground.

It is also worth remembering that much esoteric knowledge is difficult to interpret and requires expert interpreters. To some extent, though, this very esotericism can be created deliberately. Knowledge confers power and power is often be gained and jealously guarded in this manner. Any admission of the pedestrian nature of a particular type of knowledge would make it available to every one! We still see many of the vestiges of this 'form of life' in organizations the world over. Esoteric knowledge is often considered to be dangerous, particularly in the hands of the uninitiated. Secret societies are established with rites of initiation, stages of progress and secret documents to protect the knowledge and retain its power. In these contexts knowledge management tools may either be resisted or given token acceptance. Some aspects of organizational knowledge many be represented using the tools but much may be deliberately left out!

Of course, one of the most well developed sets of tools and methodologies are those the scientific method. Over many centuries this approach has been enhanced and refined. Technologies have been applied and tools developed to 'create' knowledge along with methods. In addition, there is a well-developed social context for the assessment and refinement of scientific knowledge. Scientific knowledge has to be accepted by the scientific community before it IS scientific knowledge.

The relevance of coming to understand something about what we might call scientific knowledge management is that it can direct our attention to potential gaps in our understanding of knowledge management in organizational contexts. Organizations have evolved into many interrelated 'forms of life'. The creation of functional disciplines has resulted in there being many different types of knowledge residing in organizations. Some of this knowledge certainly has the status of scientific knowledge - Research and Development departments often have strong scientific cultures. They have many of the tools that are typically used by the scientific community. On the other hand much of the marketing department's understanding of consumer behaviour may be grounded in scientific disciplines but may be just as much hunch and intuition as science.

The recognition that there are many different types of knowledge within an organization is the source of much of the richness of organizations. It is often the source of their complexity, the source of their flexible responses to the external environment, the source of competencies that are very difficult for their competition to copy. The fundamental issue at stake, however, is that we ignore such richness at our peril. If we place too much emphasis on one particular type of knowledge or knowledge culture we are likely to 'hollow out' the knowledge of the organization and leave it competitively vulnerable. Knowledge management tools must be used to explore this richness rather than be used to slavishly enforce one particular type of knowledge or knowledge culture. As I have suggested before knowledge management tools, by focusing on one particular approach to codifying knowledge can destroy the richness of the organizational knowledge ecology.

There are some more general lessons that we can learn from the above with respect to knowledge management tools.

• Many tools may have very different functions depending on the context within which they are used. For example e-mail may provide the basis for sufficiently rich communication between individuals within the scientific community. It may be a tool that facilitates the creation, refinement and transfer of knowledge in this context. In contrast, when members of the general public share e-mail it may only be the source of rumour and innuendo.
• A particular tool may enforce a particularly restricted approach on the user. This is unlikely to be because there is some inherent inflexibility in the tool itself. It is likely to be the case that in many contexts the tool is perceived in a particular way. A parallel of this problem is the basis of the socio-technical systems approach. There are many different social contexts into which a particular technology may 'fit'. The different contexts may have very different values, behaviours and, indeed knowledge.
• Great care must be taken when trying to integrate knowledge from two different communities of knowledge within an organization. This is true even when they make use of the same tools. I would hasten to stress that much greater care has to be exercised when the communities are using the same tools - there needs to be extensive investigation and negotiation between the communities to ensure the appropriate integration of knowledge - or the decision that it simply cannot be integrated.

One other word of caution with respect to knowledge cultures. I have stressed the importance of recognizing their richness of knowledge cultures within an organization. It is also important to recognize instances of the inappropriate identification of a particular knowledge culture. When investigating the knowledge cultures within the organization we must compare their knowledge practices with their understanding of these knowledge practices. For example, some groups may feel that their knowledge is essentially scientific knowledge. On closer inspection we may find that the knowledge is not open to verification, it comes from unsubstantiated sources and so on.

Before moving to consider some of the Web-based knowledge management tools that are now available to knowledge management practitioners I would like to review, in a little more detail, the various stages of knowledge management starting with knowledge generation.

Knowledge Generation
As noted above Ruggles (1997, p.2) states that knowledge generation includes the activities of knowledge creation, knowledge acquisition, knowledge synthesis, knowledge fusion, and knowledge adaptation. One of the most interesting features of most of these activities is the need for intensive communication and a culture that is accepting of new ideas and is prepared to support exploration. In addition, interestingly enough, there is need to provide barriers. New knowledge will not be created if there are not barriers to rail against. There needs be some structure, some established knowledge.

What are the tools that aid knowledge generation? Perhaps the most obvious ones are the ones that allow for the sharing of knowledge in the first place. It is only through the sharing of knowledge that we become aware of the gaps in our knowledge. In the case of many businesses and organizations it is critically necessary to be able to surface the current knowledge and assumptions of the business. It is particularly important to surface these fundamental assumptions (the tacit context within which the business operates) - the unwritten rules of the organization. In many organizations there have been many examples of traditional 'knowledge' that has been handed down from one generation the next. Sometimes this knowledge has been explicitly handed down in company manuals or in training sessions. More often than not, however, it has been embedded in company processes - hidden from view but very much there. Knowledge management tools can be used to surface this knowledge and make it available for critical scrutiny. As we will see below, the artificial intelligence community has build a variety of tools over the years that allow us to represent knowledge - these tools will become central to some aspects of knowledge management over the coming decades.

Knowledge Codification and Refinement
Traditionally we have codified knowledge in a variety of ways. Artificial intelligence research has provided us with a much clearer understanding of both the strengths and limitations of the approaches we have adopted. One popular approach is to codify our knowledge in terms of rules. This was first exemplified by Aristotle with his syllogism - the rules of correct argumentation. Rules that would guarantee that if we started with true propositions we would end up with true deductions from those true propositions. We may think of the syllogism and the rules for developing mathematical proofs as rules for preserving truth. These rules do not establish truth they only preserve truth.

Over the 80s and 90s vast numbers of researchers made use of a variety of tools to encode these rules and investigate their behaviour. Tools, such as those based on the programming language Prolog provided an unique opportunity to investigate the interaction of rules and the range of deductions that could be made from them. In some cases this lead to the refinement of the knowledge. In other cases it led to the recognition that the knowledge being investigated could only partially be represented in the form or rules or not represented at all. An interesting example of this arose in the Law. On the surface the Law would appear to be rule based and it is reasonably so in some areas. However, in many areas of the Law a very significant amount of knowledge is needed to interpret the rules and it does not appear that this knowledge can be embedded in the rules themselves.

Unfortunately we do not have time or space to investigate all the tools that are available for codifying and refining knowledge. Readers who want to explore this area are best advised to seek out the artificial intelligence literature. However, along the lines of our previous discussion a severe caution is in order about tools that can be used to codify knowledge. These tools typically provide for one way of representing knowledge (though some are somewhat more flexible). The knowledge that you wish to represent may not be representable in this way or may only be partially representable, as in the case of the Law and a rule-based approach. Further, it may take considerable skill and knowledge to be able to interpret the knowledge and represent it using a particular representation. Many of us in the expert systems field spent many years learning how to represent specific knowledge in relatively 'simple' rules. Finally, you should always be concerned that knowledge has been lost through the use of too restrictive an approach to representing that knowledge.

Knowledge transmission
As we have noted above the Web provides for the transfer of very rich information in a timely and machine-independent way. Thus, many of the tools that are used for knowledge codification and refinement can be made directly available to anyone who has access to the Web. This potentially allows for the transmission of many different varieties of knowledge.

However, to assume that the Web can 'deliver' knowledge is as naïve a belief as the belief that knowledge can be 'extracted' from individual experts and embedded in computer programs. The contextual importance of knowledge cannot be overstressed. Interestingly enough the intensively communicative nature of the Web and the Internet may allow for the building and extension of context in ways that were formerly only possible in face-to-face communities. However, I suspect that there are many other aspects of social context that can only be established through traditional social process such as assimilation. I also suspect that integrating (or at least attempting to integrate) knowledge from different knowledge communities will require extensive face-to-face interaction.

Having put some boundaries on the potential capabilities of Web-based (enabled) knowledge management tools we will now proceed to investigate some of the major types of these tools.

Web-based (enabled) information technology tools for knowledge management
The emphasis of this part of the paper is on Web-based knowledge management tools. There are a number of reasons for focusing on Web-based (or enabled tools). The most important reason is that the Web offers a very powerful platform for tools supporting all stages of knowledge management. The Web is an intensively interactive medium providing for rich communication between any user regardless of their location or equipment. This interaction can take place either synchronously or asynchronously. The Web also allows for an unprecedented degree of integration of different representational and communicational media. This allows us to make the most of existing tools while developing a variety of new tools - thus our reference below to some pre-Web tools as well as some unique Web tools.

Traditional Database Tools
Over the years more and more sophisticated database modeling tools have been developed. These tools attempt to allow users to create general data properties implicitly within the database. For example, they allow for the creation of objects that have certain properties, can communicate with other objects, and so on. Though the creation of databases we have encapsulated much knowledge of many domains. Some of the clearest examples of knowledge creation arise from the analysis of large amounts of data in databases. Giving structure to data is one of the key stages to statistical analysis. It is no surprise that at the core of the statistical analysis package SAS is a powerful relational database.

I has been argued that object-orientation, in hiding much knowledge, is a step backwards from a knowledge management perspective. I think that this is arguable - object-orientation is an approach to codifying knowledge. In some sense codification does 'hide' knowledge but it does so for a reason. It allows for the understanding of relationships and behaviour that would be difficult to interpret otherwise - by hiding some knowledge it reveals other knowledge.

Many will argue that, by themselves, these tools do not constitute knowledge management tools. They are data management tools and only become even information management tools through extensive interaction with users. As I have indicated above it is my belief that ALL knowledge management tools require extensive interaction with their users.

Process Modeling and Management Tools
In recent years more and more attention has been focused on organizational processes. In the past the major focus of process knowledge related to manufacturing processes. Processes that involved the transformation of physical material have been the focal metaphor. Tools that have been built to support these processes may encode considerable knowledge of the process. For example knowledge relating to the order in which particular activities may be carried out is implicitly present in a particular implementation of the process model. However, it is worth noting that the reasons why particular precedence relations exist may not be encoded in the process management implementation. This can cause problems when the transformation technologies change. In these cases we expect the production engineers to be able to appreciate opportunities for reengineering the process.

As we move more and more into a world where information is at the core of business and organizational processes we much represent these processes in ways that reflect the nature and characteristics of information. We also need to become more adept at building process models that integrate physical and information flows.

Almost as important as modeling the flows we need to be able to measure and control the flows - there is a vital management component. Process modeling tools must assist in the management of processes and the creation of knowledge relating to the processes and the management of those processes.

Workflow Management Tools
These tools have grown out of traditional flowcharting tools. In a sense they are the process management tools for information-intensive organizations. Workflow tools allow for the specification of the movement of documents in information processes. Interestingly enough many organizations learn about their information processes through modeling them using workflow management tools. Workflow tools can also be used to implement and manage processes.

As these tools have evolved they have begun to have capabilities for both representing both the knowledge of the workflow process and the knowledge that is processed using the workflow process. However, there is clearly much more work to be done in this area.

As I have indicate above these tools need to evolve to provide knowledge of the processes and knowledge necessary for managing the processes.

Enterprise Resource Management Tools
There is little doubt that Enterprise Resource Planning and Enterprise Resource Management (ERP/ERM) applications embed significant knowledge about the organization and, increasingly, suppliers and customers. At the centre of SAP systems are a variety of models of the organization's processes, organizational structure, strategic plans and so on. There are two key issues here. First, to what extent is this knowledge available explicitly for enquiry, modification and refinement? Generally speaking it is not possible to formulate enquiries about the nature of the processes, organizational structure and so on. The second issue relates to the ability of the various packages to deliver knowledge to the appropriate activities.

Enterprise modeling tools are being developed that provide all the modeling capabilities of ERP/ERM systems along with the explicit representation of organizational and environmental knowledge. Most of these tools are still in the research phase of their development. One system that has been implemented on a variety of test sites has been developed by Mark Fox and his co-researchers at the University of Toronto (www.eil.utoronto.ca).

One of the key challenges of ERP packages is to be able to integrate the many different types of knowledge that they represent and present it to may different types of users in a meaningful way. Further, when organizations are linked together through ERP systems even more care must be taken over integrating the knowledge of the different organizations.

Agent Tools
These tools rely on agents, relatively autonomous programs that can perform a variety of tasks. One example of the use of agents is with respect to finding information. Agents may be provided with the specifications of the information that the user is interested in and they will then search the Web and specified other databases to find the information. Early versions of information seeking agents did little more than the existing first generation search engines. New versions of information seeking agents are more 'intelligent' and are more able to identify relevant information - they are more aware of the context of queries for information and make use of this knowledge in constructing queries for databases and in selecting information from the Web.

Information agents may be facilitate initial activity by the user - as the user attempts to 'pull' information through posing particular questions. On the other hand agents may track the behaviour of users and try to anticipate the user's needs for information. These types of tool make use of a 'push' strategy.

An interesting development in the area of 'push' tools in this area is the Active Collaborative Filtering (ACF) tools. These tools, widely used by such companies as amazon.com, Musicboulevard.com and many other consumer oriented e-commerce companies, attempt to predict user interests based on the interests of users with similar profiles. This becomes an increasingly powerful technology as the number of users increases and it can also be used to develop profiles of knowledge communities.

An interesting, if somewhat breathless, discussion of the potential of ACF can be found at www.lucifer.com/~sasha/articles/ACF.html.

At another level, information-seeking agents can act in a consultative fashion with users. In this mode they are somewhat like human librarians - when the user poses a question they ask further questions of the user in order to refine the question. The refinement is developed based on the librarian's knowledge of the structure and content of the databases that are likely to be searched, metaknowledge and also, some knowledge of the domains of knowledge that are liable to be of interest to the user.

Search Engines, Navigation Tools and Portals
One of the most significant applications for the Web were search engines such as Yahoo, Excite, AltaVista and the like. There are now many thousands of search engines - some of them essentially generic while others address narrow niches. The first generation of these search engines varied in the quality of information they returned to the user. Some of the search engines performed automatic text-only searches while others relied on human "interpreters" who would access Web pages and then analyze and classify them. The second generation of search engines developed somewhat more sophistication in looking both for specific terms and also related terms - attempting in this way to try and identify more accurately what the questioner was looking for. In addition, these second-generation search engines used a variety of methods to weed out uninformative hits.

The development of these search engines continues as they begin to become, in part, knowledge navigators. This is hardly a new phenomenon and again draws on earlier work in computer science and in particular artificial intelligence. Users are provided with support as they navigate themselves through a knowledge domain in order to locate the knowledge that they are seeking. As these navigation aids become more sophisticated they are having to take account of the users initial knowledge of the domain and, in some cases, providing instruction as the search proceeds in order that the user can provide appropriate answers to guide subsequent navigation.

As an interesting sidebar it is worth noting that there is considerable concern over what is called 'deep linking' at the moment - one organization accessing another organizations Web site not through the normal route but directly to some particular page. I needn't stress that problems of context and interpretation inevitably occur in these situations regardless of issues relating to intellectual property and to tort of passing off.

In the listing in the appendix we have not provided details of these tools. We hope to provide a detailed discussion of developments in this area in the coming hear. Should you be interested in our work in this area please feel free to e-mail Anthony Wensley at wensley@home.com.

Visualizing Tools
The increasing power of computers and the development of high-resolution monitors has given us access to a variety of very powerful visualization tools. Some of these tools have been used for data visualization I areas from financial markets to molecular biology. Other tools have been developed to investigate the structure of knowledge domains and knowledge within domains.

We have not provided a detailed listing of these tools since they are only just becoming Web-enabled. We do expect that these tools will become increasingly powerful and popular over the next decade.

Collaborative Tools
As indicated above, one of the key aspects of knowledge and knowledge management is that most stages are to some extent or other collaborative. Over the years a wide variety of collaborative tools have been developed and many of them are now available through the Web. In the appendix at the end of this chapter we have provided some examples of collaborative tools but there, of course, many tools that we have not referenced. Some tools provide for setting up bulletin boards while others provide for real-time video-conferencing, whiteboards and chat rooms. The potential for the development of new collaborative tools is vast.

Virtual Reality
In addition to collaborative tools that provide support for communication directly other collaborative tools provide environments for collaboration through interactive model building and analysis. We have only just begun to tap some of the power of virtual realities in this area. Virtual realities provide an active laboratory for investigating, representing and refining knowledge. These realities can also be excellent tools for sharing knowledge.

References

A Little Knowledge is a Dangerous Thing. Dale Neef, Oxford, Butterworth-Heinemann, 1999.

Humans, Machines, and the Structure of Knowledge." Harry M. Collins. In Knowledge Management Tools. Rudy L. Ruggles (ed.). Oxford, Butterworth-Heinemann, 1997.

Knowledge Management Tools. Rudy L. Ruggles (ed.). Oxford, Butterworth-Heinemann, 1997.

Appendix

Please Note: The complete Appendix is available by downloading the original Microsoft Word document. Only the introduction is presented below.

The following survey of Web-based and Web-enabled software tools for knowledge management was compiled by my research assistant Alison Verwijk-O'Sullivan. It is presented in alphabetical order and refers to a wide range of Web or Web enabled tools. Some of these tools are basic e-mail or e-mail filtering tools some are tools for document management while others are sophisticated tools for building Intranets and analyzing their structure and performance.

The firms provided the descriptions of the products provided in the listings themselves. The assessments of the products do not necessarily represent the assessments of the authors nor should they be taken as any expressed or implied endorsement of the products by the authors.

In the listing below we have used the Ernst and Young categories of the activities involved in knowledge management to give an initial 'cut' at the capabilities of each of the following tools. As a further refinement the following categories of tools have been identified:

Acquire
Store
Deploy
Add value
Deploy

We plan to make a more refined version of this catalog available on the Web. If you are interested in this project please send an e-mail to wensley@home.com so that we can update you on the progress of the project.