Search (3 results, page 1 of 1)

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Abstract

Research on the use and users of online catalogs conducted in the early 1980s found that subject searches were the most common form of online catalog search. At the same time, many of the problems experienced by online catalog users have been traced to difficulties with the subject access mechanisms of the online catalog. Numerous proposals have been made for methods intended to improve subject access to online catalog records. These commonly involve enhancing the catalog's bibliographic records with additional terms, or incorporating subject authority files or additional thesauri in the database. Another stream of research has concentrated on applying retrieval techniques derived from information retrieval (IR) research to replace the Boolean search methods of conventional online catalog systems. This study describes the results of retrieval tests using a variety of these search methods in the CHESHIRE experimental online catalog system.

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Abstract

The Cheshire II online catalog system was designed to provide a bridge between th realms of purely bibliographical information and the rapidly expanding full-text and multimedia collections available online. It is based on a number of national and international standards for data description, communication, and interface technology. The system uses a client-server architecture with X window client communication with an SGML-based probabilistic search engine using the Z39.50 information retrieval protocol

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Abstract

Research into online catalog use and users has found some pervasive problems with subject searching in these systems. Subject searches too often fail to retrieve anything, and those that do succeed often retrieve "too much" material. This article examnies these problems and how they might be remedied. The theoretical principles for the design of effective information retrieval systems are discussed, and an experimental online catalog system based on these principles is described. The system, CHESHIRE, uses a method called "classification clustering", combined with probabilistic retrieval techniques, to provide natural language searching (which helps to reduce search failure) and to provide effective control of "information overload" in subject searching