A mathematical model of a weighted boolean retrieval system

Waller, W.G.; Kraft, Donald H.

doi:10.1016/0306-4573(79)90030-x

Cited by 123 publications

(69 citation statements)

References 8 publications

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“…In order to achieve a ranking of documents, document or query terms must be assigned weights. The definition of Boolean operators is then extended to the non-binary case (Bookstein, 1980;Salton, Fox, & Wu, 1983;Waller & Kraft, 1979), or a ranking function which can account for term weights is added to a traditional Boolean retrieval (Radecki, 1988). Theoretically, it is conceivable that under some definition of AND and OR, a user could express his contextual dependencies, but it is not clear what definition of AND and OR would allow this.…”

Section: Partial Coordination Vs Extended Booleanmentioning

confidence: 98%

Partial coordination. I. The best of pre-coordination and post-coordination

Bodoff¹,

Kambil²

1998

J. Am. Soc. Inf. Sci.

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Section: Partial Coordination Vs Extended Booleanmentioning

confidence: 98%

Partial coordination. I. The best of pre-coordination and post-coordination

Bodoff¹,

Kambil²

1998

J. Am. Soc. Inf. Sci.

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“…The main formal drawback of these models is that, to be consistent with query weight semantics, they must renounce the separability property of the wish-list (Cater & Kraft, 1989;Waller & Kraft, 1979).…”

Section: Foundations Of the Fuzzy Linguistic Modelmentioning

confidence: 99%

A fuzzy linguistic approach generalizing Boolean Information Retrieval: A model and its evaluation

Bordogna

Pasi

1993

J. Am. Soc. Inf. Sci.

249

111

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“…, named index term weight, indicating the degree of aboutness or significance F (d, t) of document d with respect to term t [11,12]. The computation of F (d, t) is generally based on the number of occurrences of t in the document d and in the whole archive D. The introduction of the index term weight made it possible to represent a document as a fuzzy set of terms [2]:…”

Section: A Graded Approachmentioning

confidence: 99%

Graded-Inclusion-Based Information Retrieval Systems

Bosc¹,

Claveau²,

Pivert³

et al. 2009

Lecture Notes in Computer Science

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Abstract. This paper investigates the use of fuzzy logic mechanisms coming from the database community, namely graded inclusions, to model the information retrieval process. In this framework, documents and queries are represented by fuzzy sets, which are paired with operations like fuzzy implications and T-norms. Through different experiments, it is shown that only some among the wide range of fuzzy operations are relevant for information retrieval. When appropriate settings are chosen, it is possible to mimic classical systems, thus yielding results rivaling those of state-of-the-art systems. These positive results validate the proposed approach, while negative ones give some insights on the properties needed by such a model. Moreover, this paper shows the added-value of this graded inclusion-based model, which gives new and theoretically grounded ways for a user to easily weight his query terms, to include negative information in his queries, or to expand them with related terms.

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A mathematical model of a weighted boolean retrieval system

Cited by 123 publications

References 8 publications

Partial coordination. I. The best of pre-coordination and post-coordination

Partial coordination. I. The best of pre-coordination and post-coordination

A fuzzy linguistic approach generalizing Boolean Information Retrieval: A model and its evaluation

Graded-Inclusion-Based Information Retrieval Systems

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