An extended fuzzy Boolean model of information retrieval revisited (original) (raw)
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Journal of Library Services and Technologies, 2020
This paper reviews the concept and goal of Information Retrieval Systems (IRSs). It also explains the synonymous concepts in Information Retrieval (IR)which include such terms as: imprecision, vagueness, uncertainty, and inconsistency. Current trends in IRSs are discussed. Fuzzy Set Theory, Fuzzy Retrieval Modelsare reviewed. The paper also discusses extensions of Fuzzy Boolean Retrieval Models including Fuzzy techniques for documents’ indexingandFlexible query languages. Fuzzy associative mechanisms were identified to include:(1)fuzzy pseudothesauri and fuzzy ontologies which can be used to contextualize the search by expanding the set of index terms of documents;(2)an alternative use of fuzzy pseudothesarui and fuzzy ontologies is to expand the query with related terms by taking into account their varying importance of an additional termand (3)fuzzy clustering techniques, where each document can be placed within several clusters with a given strength of belonging to each cluster, ...
A new fuzzy logic based information retrieval model
2008
We propose a comprehensive model of information retrieval (IR) based on Zadeh's linguistic statements. Its characteristic feature is a capability to take into account both the imprecision and uncertainty pervading the textual information representation. It extends earlier IR models based on broadly meant fuzzy logic. Moreover, some techniques for obtaining quantitative representations of documents and queries are proposed.
Fuzzy information retrieval model revisited
Fuzzy Sets and Systems, 2009
A new comprehensive model of information retrieval (IR) based on Zadeh's calculus of linguistic statements is proposed. Its characteristic and novel feature is the capability to take into account both the imprecision and uncertainty pervading the textual information representation. It extends earlier IR models based on broadly meant fuzzy logic. Moreover, some techniques for indexing documents and queries in the framework of this model are proposed. The results of the computational experiments on standard document collections are reported.
ON AN INTERPRETATION OF KEYWORDS WEIGHTS IN INFORMATION RETRIEVAL: SOME FUZZY LOGIC BASED APPROACHES
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2009
Relevant contributions of fuzzy logic to the logical models in information retrieval is studied. It makes it possible to grasp the graduality of some relevant concepts and to model both imprecision and uncertainty inherent to the retrieval process, still in the framework of the broadly meant logical approach. In this perspective we discuss various extensions to the basic Boolean model which are needed to attain such a greater expressivity. In particular, we show how the well-known semantics of keywords weights may be recovered in various fuzzy logic based information retrieval models.
Personalized information retrieval system in the framework of fuzzy logic
Expert Systems with Applications, 2008
Due to increase in web-based applications, the need for enhanced information retrieval system that accommodate user's needs become crucial. Most of commercial information retrieval system are based on standard Boolean model and at less scale vector models. Although the deficiencies of these models are now part of text-book knowledge, the development of new models still have to overcome the feasibility and testing challenge. This paper advocates a fuzzy based approach for information retrieval where a new model is put forward. Also, its feasibility and performance are demonstrated through a testing with a large-scale university database and whose results are compared to a standard commercial Boolean model.
An information retrieval model using the fuzzy proximity degree of term occurences
Proceedings of the 2005 ACM symposium on Applied computing - SAC '05, 2005
Based on the idea that the closer the query terms in a document are, the more relevant this document is, we propose a mathematical model of information retrieval based on a fuzzy proximity degree of term occurences. Our model is able to deal with Boolean queries, but contrary to the traditional extensions of the basic Boolean information retrieval model, it does not explicitly use a proximity operator. A single parameter allows to control the proximity degree required. With conjunctive queries, setting this parameter to low values requires a proximity at the phrase level and with high values, the required proximity can continuously be relaxed to the sentence or paragraph levels. We conducted some experiments and present the results.
Implementation of an efficient Fuzzy Logic based Information Retrieval
This paper exemplifies the implementation of an efficient Information Retrieval (IR) System to compute the similarity between a dataset and a query using Fuzzy Logic. TREC dataset has been used for the same purpose. The dataset is parsed to generate keywords index which is used for the similarity comparison with the user query. Each query is assigned a score value based on its fuzzy similarity with the index keywords. The relevant documents are retrieved based on the score value. The performance and accuracy of the proposed fuzzy similarity model is compared with Cosine similarity model using Precision-Recall curves. The results prove the dominance of Fuzzy Similarity based IR system.