Combination retrieval for creating knowledge from sparse document-collection

Authors:
Naohiro Matsumura;Yukio Ohsawa;Mitsuru Ishizuka
Affiliations:
PRESTO, Japan Science and Technology Corporation, Kawaguchi Center Building, 4-1-8 Honcho, Kawaguchi-Shi, Saitama 332-0012, Japan and Graduate School of Engineering, The University of Tokyo, 7-3-1 ...;PRESTO, Japan Science and Technology Corporation, Kawaguchi Center Building, 4-1-8 Honcho, Kawaguchi-Shi, Saitama 332-0012, Japan and Graduate School of Business Science, University of Tsukuba, To ...;Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Venue:
Knowledge-Based Systems
Year:
2005

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Abstract

With the variety of human life, people are interested in various matters for each one's unique reason, for which a machine maybe a better counselor than a human. This paper proposes to help user create novel knowledge by combining multiple existing documents, even if the document-collection is sparse, i.e. if a query in the domain has no corresponding answer in the collection. This novel knowledge realizes an answer to a user's unique question, which cannot be answered by a single recorded document. In the Combination Retriever implemented here, cost-based abduction is employed for selecting and combining appropriate documents for making a readable and context-reflecting answer. Empirically, Combination Retriever obtained satisfactory answers to user's unique questions.