In accordance with the need to understand the process of knowledge use, the programme of AKT is based around six challenges to ease fundamental bottlenecks in the engineering and management of knowledge. Each of these bottlenecks occurs at a vital stage in the evolution of knowledge

• Acquiring Knowledge

  Although we often suffer from a surfeit of data, we still face situations where the problem is insufficient or poorly-specified knowledge. Knowledge Acquisition sets the challenge of getting hold of the information that is around, and turning it into knowledge by making it usable. Key issues include how to make tacit knowledge explicit; how to identify gaps in knowledge already held; how to acquire and integrate knowledge from multiple sources (e.g. different people, or distributed sources on the WWW); how to acquire knowledge from different media (e.g. diagrammatic knowledge, or knowledge from unstructured text).

• Modelling Knowledge

  Knowledge modelling technologies occupy a key role; they provide a bridge between the acquisition of knowledge and its use. Knowledge model structures must be able both to act as straightforward placeholders for the acquired knowledge coming in, and to represent the knowledge so that it can be used for problem-solving. These are very different requirements, and can pull in different directions. One important knowledge modelling area is that of ontologies, which are specifications of the generic concepts, attributes, relations and axioms of a domain. Ontologies can act as placeholders and organising structures for acquired knowledge, while also providing a format for understanding how knowledge will be used.

• Reusing Knowledge

  One of the most serious impediments to cost-effective knowledge intensive system construction is that usually they are built afresh. It is unusual for problem-solving experience or domain content to be acquired and then reused, partly because knowledge tends to require different representations depending on the problem-solving that is intended to do. Understanding how to find patterns in knowledge, to allow for its storage in a library so that it can be reused when circumstances permit would save a good deal of managerial effort in reacquiring and restructuring the knowledge that had already been used in a different context.

• Retrieving Knowledge

  In any large repository retrieval of knowledge is an issue. How do we recover a subset of content relevant to a problem or task? Human knowledge is indexed by additional knowledge structures to limit and direct our search for relevant content. In some cases the process is not one of retrieval but dynamic extraction - configuring knowledge out of resources for a particular problem. Automated methods to support retrieval and extraction are vital as are architectures to integrate such capabilities.

• Publishing Knowledge

  Assuming large repositories of well-structured, well-indexed knowledge can be built we then face the problem of how best to publish or disseminate this content. Knowledge as many recognise is only effective if it is delivered in the right form, at the right place, to the right person at the right time. Different users may want to see knowledge presented and visualised in quite different ways. Getting presentation right will involve understanding the different perspectives of people with different agendas, while an understanding of knowledge content will help to ensure that important pieces of knowledge get published at the appropriate time

• Maintaining Knowledge

  Finally, having got the knowledge acquired, and having managed to retrieve and disseminate it appropriately, the last challenge is to keep the knowledge repository useful by maintaining it as it sits there. This may involve the regular updating of content as content changes. But it may also involve a deeper analysis of the knowledge content. Some content has a considerable longevity, while other knowledge dates very quickly. If a repository of knowledge is to remain active over a period of time, it is essential to know which parts of the knowledge base must be discarded and when. Other problems involved in maintenance include verifying and validating the content, and certifying its safety.