Testing of a natural language retrieval system for a full text knowledge base

Abstract

Abstract “A Navigator of N atural L anguage Organized D ata” (ANNOD) is a retrieval system which combines use of probabilistic, linguistic, and empirical means to rank individual paragraphs of full text for their similarity to natural language queries proposed by users. ANNOD includes common word deletion, word root isolation, query expansion by a thesaurus, and application of a complex empirical matching (ranking) algorithm. The Hepatitis Knowledge Base, the text of a prototype information system, was the file used for testing ANNOD. Responses to a series of users' unrestricted natural language queries were evaluated by three testers. Information needed to answer 85 to 95‰ of the queries was located and displayed in the first few selected paragraphs. It was successful in locating information in both the classified (listed in Table of Contents) and unclassified portions of text. Development of this retrieval system resulted from the complementarity of and interaction between computer science and medical domain expert knowledge. Extension of these techniques to larger knowledge bases is needed to clarify their proper role.