Deriving Expectations to Guide Knowledge Base Creation

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Knowledge Representation

Successful approaches to developing knowledge acquisition tools use expectations of what the user has to add or may want to add, based on how new knowledge fits within a knowledge base that already exists. When a knowledge base is first created or undergoes significant extensions and changes, these tools cannot provide much support. This paper presents an approach to creating expectations when a new knowledge base is built, and describes a knowledge acquisition tool that we implemented using this approach that supports users in creating problem-solving knowledge. As the knowledge base grows, the knowledge acquisition tool derives more frequent and more reliable expectations that result from enforcing constraints in the knowledge representation system, looking for missing pieces of knowledge in the knowledge base, and working out incrementally the inter-dependencies among the different components of the knowledge base. Our preliminary evaluations show a thirty percent time savings during knowledge acquisition. Moreover, by providing tools to support the initial phases of knowledge base development many mistakes are detected early on and even avoided altogether. We believe that our approach contributes to improving the quality of the knowledge acquisition process and of the resulting knowledge-based systems as well.