Combination Retrieval for Creating Knowledge from Sparse Document Collection

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. q 2005 Published by Elsevier B.V.

• Type: Conference
• Year: 2000
• Where: DIS
• Authors: Naohiro Matsumura, Yukio Ohsawa
• Keywords: Combination Retriever, DIS 2000, One’s Unique Reason, Theoretical Computer Science, User’s Unique Questions