Combination retrieval for creating knowledge from sparse document-collection

Naohiro Matsumura, Yukio Ohsawa, Mitsuru Ishizuka

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)327-333
Number of pages7
JournalKnowledge-Based Systems
Volume18
Issue number7
DOIs
Publication statusPublished - 2005 Nov
Externally publishedYes

Keywords

  • Cost-based abduction
  • Information retrieval
  • Knowledge creation

ASJC Scopus subject areas

  • Artificial Intelligence

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