InforLorV4, Main, Exploration, bibRecord, 000915

A semantic approach to concept lattice-based information retrieval

Identifieur interne : 000915 ( Main/Exploration ); précédent : 000914; suivant : 000916

A semantic approach to concept lattice-based information retrieval

Auteurs : Victor Codocedo [France] ; Ioanna Lykourentzou [Luxembourg (pays)] ; Amedeo Napoli [France]

Source :

Annals of Mathematics and Artificial Intelligence [ 1012-2443 ] ; 2014-10-01.

RBID : Hal:hal-01095859

English descriptors

mix :
- concept lattices, cousin concepts, formal concept analysis, information retrieval, similarity.

Abstract

The volume of available information is growing, especially on the web, and in parallel the questions of the users are changing and becoming harder to satisfy. Thus there is a need for organizing the available information in a meaningful way in order to guide and improve document indexing for information retrieval applications taking into account more complex data such as semantic relations. In this paper we show that Formal Concept Analysis (FCA) and concept lattices provide a suitable and powerful support for such a task. Accordingly, we use FCA to compute a concept lattice, which is considered both a semantic index to organize documents and a search space to model terms. We introduce the notions of cousin concepts and classification-based reasoning for navigating the concept lattice and retrieve relevant information based on the content of concepts. Finally, we detail a real-world experiment and show that the present approach has very good capabilities for semantic indexing and document retrieval.

Url:

https://hal.inria.fr/hal-01095859

DOI: 10.1007/s10472-014-9403-0

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 000953
to stream Hal, to step Curation: 000953
to stream Hal, to step Checkpoint: 000849
to stream Main, to step Merge: 000916
to stream Main, to step Curation: 000915

Le document en format XML

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<orgName>Laboratoire Lorrain de Recherche en Informatique et ses Applications</orgName>
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<date type="start">2012-01-01</date>
<desc><address><addrLine>Campus Scientifique BP 239 54506 Vandoeuvre-lès-Nancy Cedex</addrLine>
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<orgName>Université de Lorraine</orgName>
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<date type="start">2012-01-01</date>
<desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine>
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<ref type="url">http://www.univ-lorraine.fr/</ref>
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<orgName>Centre National de la Recherche Scientifique</orgName>
<orgName type="acronym">CNRS</orgName>
<date type="start">1939-10-19</date>
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</address>
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<country>France</country>
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<region type="old region" nuts="2">Lorraine (région)</region>
</placeName>
<orgName type="university">Université de Lorraine</orgName>
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<idno type="DOI">10.1007/s10472-014-9403-0</idno>
<series><title level="j">Annals of Mathematics and Artificial Intelligence</title>
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<imprint><date type="datePub">2014-10-01</date>
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<profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>concept lattices</term>
<term>cousin concepts</term>
<term>formal concept analysis</term>
<term>information retrieval</term>
<term>similarity</term>
</keywords>
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<front><div type="abstract" xml:lang="en">The volume of available information is growing, especially on the web, and in parallel the questions of the users are changing and becoming harder to satisfy. Thus there is a need for organizing the available information in a meaningful way in order to guide and improve document indexing for information retrieval applications taking into account more complex data such as semantic relations. In this paper we show that Formal Concept Analysis (FCA) and concept lattices provide a suitable and powerful support for such a task. Accordingly, we use FCA to compute a concept lattice, which is considered both a semantic index to organize documents and a search space to model terms. We introduce the notions of cousin concepts and classification-based reasoning for navigating the concept lattice and retrieve relevant information based on the content of concepts. Finally, we detail a real-world experiment and show that the present approach has very good capabilities for semantic indexing and document retrieval.</div>
</front>
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<affiliations><list><country><li>France</li>
<li>Luxembourg (pays)</li>
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<region><li>Grand Est</li>
<li>Lorraine (région)</li>
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<settlement><li>Metz</li>
<li>Nancy</li>
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<orgName><li>Université de Lorraine</li>
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<tree><country name="France"><region name="Grand Est"><name sortKey="Codocedo, Victor" sort="Codocedo, Victor" uniqKey="Codocedo V" first="Victor" last="Codocedo">Victor Codocedo</name>
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</country>
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A semantic approach to concept lattice-based information retrieval

A semantic approach to concept lattice-based information retrieval

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