An extended fuzzy linguistic approach to generalize boolean information retrieval

Kraft, Donald H.; Bordogna, Gloria; Pasi, Gabriella

doi:10.1016/1069-0115(94)90032-9

Cited by 50 publications

(46 citation statements)

References 12 publications

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“…and c i , for example, as b ϭ round~26b Ϫ a6/T !. We should point out that whereas the traditional threshold matching function are always nondecreasing, 14 g is nondecreasing on the right of the midterm and decreasing on the left of the midterm in order to be consistent with the meaning of the symmetrical threshold semantics. 2!…”

Section: Evaluation Subsystemmentioning

confidence: 94%

“…According to the symmetrical threshold semantics the evaluation subsystem assumes that users may search for documents with a minimally acceptable presence of one term in their representations~as happens in the classical interpretation 14 In the following subsection, we try to overcome these problems by defining a new threshold matching function.…”

Section: Problems Of the Symmetrical Threshold Semantics Modeled By Tmentioning

confidence: 99%

“…There are four semantic possibilities 1,4,14 : weights~i! as a measure of the importance of a specific element in representing the query,~ii!…”

Section: Introductionmentioning

confidence: 99%

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Tuning the matching function for a threshold weighting semantics in a linguistic information retrieval system

López-Herrera

Porcel

2005

Int. J. Intell. Syst.

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Information retrieval is an activity that attempts to produce documents that better fulfill user information needs. To achieve this activity an information retrieval system uses matching functions that specify the degree of relevance of a document with respect to a user query. Assuming linguistic-weighted queries we present a new linguistic matching function for a threshold weighting semantics that is defined using a 2-tuple fuzzy linguistic approach~Herrera F, Martínez L. We show that it simplifies the processes of computing in the retrieval activity, avoids the loss of precision in final results, and, consequently, can help to improve the users' satisfaction.

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Section: Evaluation Subsystemmentioning

confidence: 94%

Section: Problems Of the Symmetrical Threshold Semantics Modeled By Tmentioning

confidence: 99%

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Tuning the matching function for a threshold weighting semantics in a linguistic information retrieval system

López-Herrera

Porcel

2005

Int. J. Intell. Syst.

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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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“…Twenty (20) queries were used in the evaluation, and some examples of the queries used are given in Table 2. In order to evaluate the performance of our proposed fuzzy semantic information retrieval model, a comparison of results was accomplished with respect to the fuzzy IR algorithm developed by Kraft et al (1994) and the boolean IR system, using the standard recall and precision evaluations by computing the precision and recall at various cut-off points, where the precision is determined at various recall levels. The equations describing the two measures are given in Equations (3) and (4).…”

Section: Performance Evaluationmentioning

confidence: 99%

A Fuzzy Semantic Information Retrieval System for Transactional Applications

Ajayi¹,

Soriyan²,

Aderounmu

2009

Data Sci. J.

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In this paper, we present an information retrieval system based on the concept of fuzzy logic to relate vague and uncertain objects with un-sharp boundaries. The simple but comprehensive user interface of the system permits the entering of uncertain specifications in query forms. The system was modelled and simulated in a Matlab environment; its implementation was carried out using Borland C++ Builder. The result of the performance measure of the system using precision and recall rates is encouraging. Similarly, the smaller amount of more precise information retrieved by the system will positively impact the response time perceived by the users.Keywords: Fuzzy set, Query, Information, Fuzzy Inference Engine, Response time INTRODUCTIONInformation Retrieval (IR) is the science of searching for information in documents, searching for metadata which describe documents, or searching within databases, whether relational stand-alone databases or a hypertextually-networked database such as the World Wide Web. The core functionality of an IR system is the retrieval of data from a database whose abstraction matches the description of an ideal object, inferred from a query (Bellman et al., 1992). A complex algorithm is used to search through the information, retrieve, and deliver the results to the user.Traditional information retrieval models as employed in current search engines typically express a query as a set of keywords in which Boolean expressions involving words are used as terms to find information (Huang & Tsai, 2005). These keyword indexing systems and Boolean logic queries are sometimes equipped with statistical methods (Buckley & Fuhr, 1990), for instance, using frequency of occurrence of a keyword to strengthen the relationship between keyword and object. This model, called the keyword-based information retrieval model, uses keyword lists to describe the contents of information objects. The keyword list is a description that does not say anything about semantic relationships between keywords. According to Baeza-Yates and Ribiero (1999) the simplicity of these models usually prevents the formulation of more elaborate querying tasks. In addition, they give unsatisfactory query results when the semantic content of the query forms can not be easily represented.In this study, a fuzzy semantic information retrieval model is developed for query tasks that can not be translated into clear forms. For instance, a potential buyer may be interested in an elementary Java textbook that introduces graphics programming with a pinch of data structure together with a bit of design patterns. Formulating a query using conventional retrieval models to describe the buyer's information needs specified above is difficult due to the ambiguity introduced in the specification of intensity of the data structure content and the concentration of design patterns to be included in said textbook. In a related manner, the use of advanced features of a typical search engine by users has always been limited because most users do not kn...

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An extended fuzzy linguistic approach to generalize boolean information retrieval

Cited by 50 publications

References 12 publications

Tuning the matching function for a threshold weighting semantics in a linguistic information retrieval system

Tuning the matching function for a threshold weighting semantics in a linguistic information retrieval system

A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling

A Fuzzy Semantic Information Retrieval System for Transactional Applications

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