Terminological knowledge structure for intermediary expert systems

Fidel, Raya; Efthimiadis, Efthimis N.

doi:10.1016/0306-4573(95)80003-c

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…The exploitation of multiple evidence has been advocated also for query expansion, by emphasizing the advantages of using multiple query representations (Turtle andCroft 1991, Belkin et al 1993) or multiple sources of terminological knowledge (Fidel andEfthimiadis 1995, Spink andSaracevic 1997). In this case, the representations are either provided by the user or generated by the systems, while the evidence combination is typically performed manually.…”

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confidence: 99%

Improving retrieval feedback with multiple term-ranking function combination

Carpineto

Romano

Giannini

2002

ACM Trans. Inf. Syst.

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and VITTORIO GIANNINI WIND Telecomunicazioni S.p.A ________________________________________________________________________In this paper we consider methods for automatic query expansion from top retrieved documents (i.e., retrieval feedback) which make use of various functions for scoring expansion terms within Rocchio's classical reweighting scheme. An analytical comparison shows that the retrieval performance of methods based on distinct term-scoring functions is comparable on the whole query set but considerably differs on single queries, consistent with the fact that the ordered sets of expansion terms suggested for each query by the different functions are largely uncorrelated. Motivated by these findings, we argue that the results of multiple functions can be merged, by analogy with ensembling classifiers, and present a simple combination technique based on the rank values of the suggested terms. The combined retrieval feedback method is effective not only with respect to unexpanded queries but also to any individual method, with notable improvements on the system's precision. Furthermore, the combined method is robust with respect to variation of experimental parameters and it is beneficial even when the same information needs are expressed with shorter queries.

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confidence: 99%

Improving retrieval feedback with multiple term-ranking function combination

Carpineto

Romano

Giannini

2002

ACM Trans. Inf. Syst.

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“…Brajnik et al, 1996;Fidel & Efthimiadis, 1995). In an early online study, Penniman (1982) analyzed Medline transaction log data, with a view to identifying patterns of interaction and ultimately facilitating automatic support.…”

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confidence: 99%

A reference model for user‐system interaction in thesaurus‐based searching

Blocks

Cunliffe

Tudhope

2006

J. Am. Soc. Inf. Sci.

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when interacting with a search system augmented with a thesaurus. A basic search scenario illustrates this process through the model. Graphical and textual depictions of the model are complemented by a concise matrix representation for evaluation purposes. Potential problems at different stages of the search process are discussed, together with possibilities for system developers. The aim is to set out a framework of processes, decisions, and risks involved in thesaurus-based search, within which system developers can consider potential avenues for support.

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Information science

Saračević

1999

J. Am. Soc. Inf. Sci.

270

155

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This essay is a personal analysis of information science as a field of scientific inquiry and professional practice that has evolved over the past half‐century. Various sections examine the origin of information science in respect to the problems of information explosion; the social role of the field; the nature of “information” in information science; the structure of the field in terms of problems addressed; evolutionary trends in information retrieval as a major branch of information science; the relation of information science to other fields, most notably librarianship and computer science; and educational models and issues. Conclusions explore some dominant trends affecting the field.

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Terminological knowledge structure for intermediary expert systems

Cited by 5 publications

References 28 publications

Improving retrieval feedback with multiple term-ranking function combination

Improving retrieval feedback with multiple term-ranking function combination

A reference model for user‐system interaction in thesaurus‐based searching

Information science

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