Search (12 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

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

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Abstract

The Cheshire 2 project is developing a next generation online catalogue and full text information retrieval system using advanced information retrieval techniques. It is being deployed at the University of California Berkeley Astronomy-Mathematics-Statistics Library, USA, and its use and acceptance is being evaluated using transaction monitoring and questionnaires. Describes the system architecture and user evaluation tools

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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

The Cheshire II system was originally developed to provide a bridge from conventional online library catalogs to full-text online resources. Recently we have begun using the system to implement full-text and fielded searching of bibliographic information for the UC Berkeley Digital Library Initiative project sponsored by NSF, NASA and ARPA. The Cheshire system is also being used to provide scalable performance for image querying using the "Blobworld" image representation. This paper will review the characteristics of the Cheshire II system and examine its performance and behavior when applied to a collection of large full-text documents in the TREC Interactive Retrieval Track and its performance in Blobworld image searching

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Abstract

Search index usage in a large university online catalog system over a six-year period (representing about 15,3 million searches) was investigated using transaction monitor data. Mathematical models of trends and patterns in the data were developed and tested using regression techniques. The results of the analyses show a consistent decline in the frequency of subject index use by online catalog users, with a corresponding increase in the frequency of title keyword searching. Significant annual patterns in index usage were also identified. Analysis of the transaction data, and related previous studies of online catalog users, suggest a number of factors contributing to the decline in subject search frequency. Chief among these factors are user difficulties in formulating subject queries with LCSH, leading to search failure, and the problem of "information overload" as database size increases. This article presents the models and results of the transaction log analysis, discusses the underlying problems with subject searching contributing to the observed decline, and reviews some proposed improvements to online catalog systems to aid in overcoming these problems

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Abstract

Information retrieval (IR) systems aim to select relevant material from large collections of information in response to user queries. The approaches used to accomplish this have been the focus of much research and development over the past 50 years, and have led to the algorithms underlying many commercial and Web-based search engines today. This entry describes the common components that go into the design of IR systems (from text processing to inverted file indexes). The major classes (or models) of retrieval algorithms (Boolean, vector, and probabilistic) are described along with formal definitions of the basic form of these algorithms and some of the variations in common use in IR research. In addition, the entry examines query expansion techniques, and in particular relevance feedback, and how they are used in IR systems.

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Abstract

Hypertext is an old concept that only recently has been demonstrated in working systems. Examines the conceptual basis of hypertext, reviews some representative hypertext systems, and discusses some of the problems to be faced when hypertext techniques are applied to large-scale information systems. Techniques developed in information retrieval research are seen as useful complements to hypertext that may remedy some of these problems.

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Abstract

This article presents the results of research into the automatic selection of Library of Congress Classification numbers based on the titles and subject headings in MARC records. The method used in this study was based on partial match retrieval techniques using various elements of new recors (i.e., those to be classified) as "queries", and a test database of classification clusters generated from previously classified MARC records. Sixty individual methods for automatic classification were tested on a set of 283 new records, using all combinations of four different partial match methods, five query types, and three representations of search terms. The results indicate that if the best method for a particular case can be determined, then up to 86% of the new records may be correctly classified. The single method with the best accuracy was able to select the correct classification for about 46% of the new records.