El.pub Analytic Issue Number 9
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|Introduction | KM development | KM in practice | Implications for RTD - measurement and focus | Implications for RTD - technology | Conclusion | Comment on this issue | Annex: Knowledge management|
There is a tendency in RTD to focus on technology (particularly what might loosely be called AI) without having a clear idea of what it is to be used for or how to measure its effectiveness. This may be coupled with attempts to transfer technologies that haven't worked well in the past into a new area as though they were new ideas. Groups that want to re-label their work in this way should have to provide an ab initio justification for funding in KM. More importantly RTD of this sort should be linked closely to a working KM environment in some organisation outside the research group so that some measure of success is generated not just by the research workers but by the users. The failure of generic solutions to show any success in KM provides the principal justification for being wary of proposals for such research.
Some of the more exciting ideas that are being proposed by the 'machine intelligence' community need to be carefully assessed, particularly in relation to 'automated' systems. It is difficult to automate something that is not well understood. There is very little evidence that automation works in cognitive science if we look back at the development record of, for example, natural language understanding. There is a need to orient this type of work towards feasibility studies and to identify key problems in projects which if not solved quickly will invalidate the whole approach (such as scalability).
The importance of standards such as XML lies in their use in achieving interoperability and interconnection of applications and basic knowledge stores. One of the problems with standardising terminology, however, is the proliferation of incompatible 'views' represented in alternative ontologies and DTDs, a problem that was identified with SGML and has yet to be properly addressed. This latter difficulty is already partially visible in the problem of persuading people to take an active part in KM projects where sharing implies changing individual views. Another long-term difficulty lies in the cost of creating the mark-up that is the key to the effective use of computerised systems. Again this was a problem identified with SGML work, probably the main reason for its commercial failure. The idea that semantic mark-up can be generated automatically has not been demonstrated. Indeed, if it were possible, it is difficult to see the need for it, as the methodology could be applied directly to solve the problems mark-up is intended to avoid.
KM requires a different perspective to IT and it is not sufficient to simply re-iterate the need for usability, interoperability and standards. Their place in KM requires a re-focussing of effort and even perhaps, methods and concepts. Standards like Dublin core for example are essentially information-centric rather than knowledge-centric. Groups working in this area might consider how to develop a new focus for some of their work.
Candidates for large projects might be sector level efforts to evaluate the use of experimental KM methods. The sort of project grouping and development that the GEN (engineering) projects created would be a good example (transferred to the KM area). As sharing knowledge between suppliers, manufacturers and customers is one of the aims of KM, a community such as European aero-space engineering might be a suitable group. One of the aims of such projects should be to share knowledge about KM effectiveness.
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