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2009•Unpublished venueRequires access

A Scalable Problem-Solver for Large Knowledge-Bases

Shaw‐Yi Chaw, Ken Barker, Bruce Porter, Dan Tecuci, Peter Z. Yeh

Open publisher page 1 citations

Abstract

We describe a problem solver built to answer questions like those on advanced placement exams using knowledge bases authored by domain experts. The problem solver is designed to work independently of any particular knowledge base or domain. Given a question, the problem solver identifies those portions of the knowledge base that are relevant to the question. We found that simple heuristics for judging relevance significantly improved performance, with no drop in coverage.

About this research paper

What this paper is about

We describe a problem solver built to answer questions like those on advanced placement exams using knowledge bases authored by domain experts. The problem solver is designed to work independently of any particular knowledge base or domain. Given a question, the problem solver identifies those portions of the knowledge base that are relevant to the question. We found that simple heuristics for judging relevance significantly improved performance, with no drop in coverage.

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OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

We describe a problem solver built to answer questions like those on advanced placement exams using knowledge bases authored by domain experts. The problem solver is designed to work independently of any particular knowledge base or domain. Given a question, the problem solver identifies those portions of the knowledge base that are relevant to the question. We found that simple heuristics for judging relevance significantly improved performance, with no drop in coverage.

Key concepts: Solver, Problem solver, Heuristics, Computer science, Domain (mathematical analysis), Scalability, Knowledge base, Relevance (law)

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