Supporting Start-to-Finish Development of Knowledge Bases

Authors:
Ray Bareiss;Bruce W. Porter;Kenneth S. Murray
Affiliations:
Computer Science Department, Vanderbilt University, Nashville, TN 37235. BAREISS@VUSE.VANDERBILT.EDU;Computer Sciences Department, University of Texas, Austin, TX 78712. PORTER@CS.UTEXAS.EDU;Computer Sciences Department, University of Texas, Austin, TX 78712. MURRAY@CS.UTEXAS.EDU
Venue:
Machine Learning
Year:
1989

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Abstract

Developing knowledge bases using knowledge-acquisition tools is difficult because each stage of development requires performing a distinct knowledge-acquisition task. This paper describes these different tasks and surveys current tools that perform them. It also addresses two issues confronting tools for start-to-finish development of knowledge bases. The first issue is how to support multiple stages of development. This paper describes Protos, a knowledge-acquisition tool that adjusts the training it expects and assistance it provides as its knowledge grows. The second issue is how to integrate new information into a large knowledge base. This issue is addressed in the description of a second tool, KI, that evaluates new information to determine its consequences for existing knowledge.