Threshold values and Boolean retrieval systems

Buell, Duncan A.; Kraft, Donald H.

doi:10.1016/s0306-4573(81)80004-0

Cited by 82 publications

(21 citation statements)

References 11 publications

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“…This link is more evident with the cardinalitybased model corresponding to formula (9). Indeed, the aggregation function is also the sum, and the matching function is a t-norm, which may be the product.…”

Section: Fuzzy Generalizationmentioning

confidence: 90%

Different interpretations of fuzzy gradual-inclusion-based IR models

Ughetto

Claveau

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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Recently, a theoretical fuzzy IR system, based on gradual inclusion measures, has been proposed [1]. In this model, derived from the division of fuzzy relations, the gradual inclusion of a query in a document is modeled by a fuzzy implication. In previous papers, we have shown that, under some assumptions, this model can be seen as a Vector Space Model. This paper also studies other interpretations of our fuzzy IR models based on gradual inclusions. It is shown that the fuzzy models can be interpreted as language models for IR. The links with logical models to IR are also recalled. More generally, this paper discusses the links between these models, shown from the point of view of our fuzzy models.

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Section: Fuzzy Generalizationmentioning

confidence: 90%

Different interpretations of fuzzy gradual-inclusion-based IR models

Ughetto

Claveau

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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“…In 82 Donald H. Kraft, Gloria Bordogna and Gabriella Pasi redefine the previous fuzzy linguistic IRS 81 by introducing a new threshold semantics in a linguistic context which was obtained by combination of both, the ideal semantics 58 and threshold semantics 60 .…”

Section: Fuzzy Linguistic Irss Based On Classical Flmmentioning

confidence: 99%

A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling

Herrera‐Viedma¹,

López-Herrera²

2010

IJCIS

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This paper presents a survey of some fuzzy linguistic information access systems. The review shows information retrieval systems, filtering systems, recommender systems, and web quality evaluation tools, which are based on tools of fuzzy linguistic modelling. The fuzzy linguistic modelling allows us to represent and manage the subjectivity, vagueness and imprecision that is intrinsic and characteristic of the processes of information searching, and, in such a way, the developed systems allow users the access to quality information in a flexible and user-adapted way.

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“…The four kind of semantics with different interpretations or matching functions have been considered in our computer-supported learning system for FIRSs, i.e., classical threshold semantics (Buell and Kraft 1981;Waller and Kraft 1979), symmetrical threshold semantics (Herrera-Viedma 2001b), improved threshold semantics (Herrera-Viedma et al 2005), relative importance semantics (Herrera-Viedma et al 2005;Yager 1987), improved relative importance semantics (Herrera-Viedma 2001a;Herrera-Viedma et al 2003), classical perfection semantics (Bordogna and Pasi 1993), non-symmetrical perfection semantics (Kraft et al 1994), quantitative semantics (Herrera-Viedma 2001b).…”

Section: Query Evaluation Proceduresmentioning

confidence: 99%

“…For example, for threshold semantics we can use three different threshold proposals: classical threshold semantics (Buell and Kraft 1981;Waller and Kraft 1979), symmetrical threshold semantics (Herrera-Viedma 2001b) and improved threshold semantics (Herrera-Viedma et al 2005). To understand their different meanings we have observed that the students need to process many examples and compare the results continuously, and it is very difficult to get it in blackboard classes.…”

Section: Teaching Problems For Firssmentioning

confidence: 99%

A computer-supported learning system to help teachers to teach Fuzzy Information Retrieval Systems

et al. 2009

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This paper describes a computer-supported learning system to teach students the principles and concepts of Fuzzy Information Retrieval Systems based on weighted queries. This tool is used to support the teacher's activity in the degree course Information Retrieval Systems Based on Artificial Intelligence at the Faculty of Library and Information Sciences at the University of Granada. Learning of languages of weighted queries in Fuzzy Information Retrieval Systems is complex because it is very difficult to understand the different semantics that could be associated to the weights of queries together with their respective strategies of query evaluation. We have developed and implemented this computer-supported education system because it allows to support the teacher's activity in the classroom to teach the use of weighted queries in FIRSs and it helps students to develop self-learning processes on the use of such queries. We have evaluated the performance of its use in the learning process according to the students' perceptions and their results obtained in the course's exams. We have observed that using this software tool the students learn better the management of the weighted query languages and then their performance in the exams is improved.

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Threshold values and Boolean retrieval systems

Cited by 82 publications

References 11 publications

Different interpretations of fuzzy gradual-inclusion-based IR models

Different interpretations of fuzzy gradual-inclusion-based IR models

A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling

A computer-supported learning system to help teachers to teach Fuzzy Information Retrieval Systems

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