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A Knowledge Entry System for Subject Matter Experts

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Essay title: A Knowledge Entry System for Subject Matter Experts

The High Performance Knowledge Bases (HPKB) project demonstrated that the teams of knowledge engineers working together could create knowledge bases (KBs) roughly at the rate of 10K axioms/year for a pre-specified task and evaluation criteria. The HPKB effort showed that it is possible to create KBs by reusing the content of knowledge libraries, and it demonstrated reuse rates ranging from 25% to 100%, depending on the application and the knowledge engineer. It was acknowledged that the ability of a subject matter expert (SME) to directly enter knowledge is essential to improve the KB construction rates.

The SRI team is developing a system for direct knowledge entry by SMEs as an integrated team of technology developers. The SRI team includes Boeing, Information Sciences Institute (ISI) at University of Southern California, Northwestern University, Pacific Sierra Research (PSR), Stanford University, University of Massachusetts at Amherst, University of Texas at Austin, and University of West Florida. Knowledge Systems Laboratory at Stanford, Pragati Systems, and Massachusetts Insititute of Technology joined the team after the contract award.

The claim of this effort is that SMEs, unassisted by AI technologists, can assemble models of mechanisms and processes from components. These models are both declarative and executable, so questions about the mechanisms and processes can be answered by conventional inference methods (for example, theorem proving and taxonomic inference) and by various task-specific methods (for example, simulation, analogical reasoning, and problem-solving methods). A related claim is that relatively few components, perhaps a few thousand, are sufficient for SMEs to assemble models of virtually any mechanism or process. We claim that these components are independent of domain, and that assembly from components instantiated to a domain is a natural way for SMEs to create KB content.

The research in this project exploits and extends previous work in the HPKB project, as well as work in process description languages, qualitative physics, systems dynamics, and simulation. One scientific innovation, and the principal extension to Cyc and the "HPKB

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