Fuzzy information retrieval

Cross, Valerie

doi:10.1007/bf01014019

Cited by 47 publications

(24 citation statements)

References 41 publications

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“…8) would ask whether region1 is Sea and Sky to a degree greater than or equal to 0.8. If there are assertions in the ABox of our that satisfy this query (i.e.…”

Section: Fire Reasoning Servicesmentioning

confidence: 99%

Image indexing and retrieval using expressive fuzzy description logics

Simou

Athanasiadis

Stoilos

et al. 2008

SIViP

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The effective management and exploitation of multimedia documents requires the extraction of the underlying semantics. Multimedia analysis algorithms can produce fairly rich, though imprecise information about a multimedia document which most of the times remains unexploited. In this paper we propose a methodology for semantic indexing and retrieval of images, based on techniques of image segmentation and classification combined with fuzzy reasoning. In the proposed knowledge-assisted analysis architecture a segmentation algorithm firstly generates a set of over-segmented regions. After that, a region classification process is employed to assign semantic labels using a confidence degree and simultaneously merge regions based on their semantic similarity. This information comprises the assertional component of a fuzzy knowledge base which is used for the refinement of mistakenly classified regions and also for the extraction of rich implicit knowledge used for global image classification. This knowledge about images is stored in a semantic repository permitting image retrieval and ranking.

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“…8) would ask whether region1 is Sea and Sky to a degree greater than or equal to 0.8. If there are assertions in the ABox of our that satisfy this query (i.e.…”

Section: Fire Reasoning Servicesmentioning

confidence: 99%

Image indexing and retrieval using expressive fuzzy description logics

Simou

Athanasiadis

Stoilos

et al. 2008

SIViP

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“…It operates in a different way depending on the interpretation associated to the numeric weights included in the query (the interested reader can refer to [4,6] to get knowledge about the three existing approaches). In this paper, we consider the importance interpretation, where the weights represent the relative importance of each term in the query.…”

Section: Matching Mechanismmentioning

confidence: 99%

“…On the one hand, most of the commercial IRSs used in corporate intranet, as well as many Internet search engines, are based on the classical Boolean IR model [36], which presents some limitations. Due to this fact, some paradigms have been designed to extend this retrieval model and overcome its problems, such as the vector space [30] or the fuzzy IR (FIR) models [4,6].…”

Section: Introductionmentioning

confidence: 99%

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

Cordón¹,

Moya

Zarco

2002

Soft Computing - A Fusion of Foundations, Methodologies and App

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Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.

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“…Fuzzy Set Theory 50 has been used in order to achieve a mathematical formalization of the use of weights for handling uncertain information in all information representation levels of an IRS 51,52,53,54,55,56 . Particularly, we should point out that we can find in the literature some fuzzy IRSs enriched with weighted query languages 57,58,59,60 that increase the expressiveness of the traditional Boolean query languages 49 , allow users represent better in the queries their concept of relevance, and improve the effectiveness of IRSs.…”

Section: Evaluation Representation Levelmentioning

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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Fuzzy information retrieval

Cited by 47 publications

References 41 publications

Image indexing and retrieval using expressive fuzzy description logics

Image indexing and retrieval using expressive fuzzy description logics

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling

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