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.