Knowledge-based search tactics for an intelligent intermediary system

Smith, Philip J.; Shute, Steven J.; Galdes, Beb; Chignell, Mark

doi:10.1145/65943.65947

Cited by 53 publications

(18 citation statements)

References 25 publications

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“…The system has a knowledge base of search strategies and term classifications similar to a thesaurus. EP-X (Smith et al, 1989) is a prototype knowledgebased system that assists in searching environmental pollution literature. This system makes extensive use of domain knowledge, represented as hierarchically defined semantic primitives and frames.…”

Section: Knowledge-based Systems In Irmentioning

confidence: 99%

Machine learning for information retrieval: Neural networks, symbolic learning, and genetic algorithms

Chen

1995

J. Am. Soc. Inf. Sci.

197

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Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 198Os, knowledge-based techniques also made an impressive contribution to "intelligent" information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution-based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.

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Section: Knowledge-based Systems In Irmentioning

confidence: 99%

Machine learning for information retrieval: Neural networks, symbolic learning, and genetic algorithms

Chen

1995

J. Am. Soc. Inf. Sci.

197

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“…By analyzing discourses between an expert intermediary and 17 real information seekers, Smith et al (1989) identify a set of search tactics. They noted when each of these tactics was applied, and whether the intermediary used the tactic spontaneously or in response to some cue in the retrieved document.…”

Section: Related Workmentioning

confidence: 99%

“…The author defines rules for deciding when to use textwords or descriptors, or both, to search indexed databases, and guidelines for including thesaural relationships. In contrast to the tactics proposed by Smith et al (1989), the emphasis is on identifying techniques which are domain independent.…”

Section: Related Workmentioning

confidence: 99%

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An expert system for automatic query reformation

Gauch

Smith

1993

J. Am. Soc. Inf. Sci.

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Unfamiliaritywith search tactics creates difficulties for many users of online retrieval systems. User observations indicate that even experienced searchers use vocabulary incorrectly and rarely reformulate their queries. To address these problems, an expert system for online search assistance was developed. This prototype automatically reformulates queries to improve the search results, and ranks the retrieved passages to speed the identification of relevant information. Users' search performance using the expert system was compared with their search performance on their own, and their search performance using an online thesaurus. The following conclusions were reached: (1) The expert system significantly reduced the number of queries necessary to find relevant passages compared with the user searching alone or with the thesaurus. (2) The expert system produced marginally significant improvements in precision compared with the user searching on their own. There was no significant difference in the recall achieved by the three system configurations. (3) Overall, the expert system ranked relevant passages above irrelevant passages. 0 1993 John Wiley & Sons, Inc.

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“…Smith, et. a] [8,9] have described a knowledge based search intermediary called EP-X that is designed to facilitate information retrieval from a thesaurus type of system. Documents were represented as frames with a list of title, authors, title, etc.…”

Section: Examples Of Intelligent Databasesmentioning

confidence: 99%