Neural-Symbolic Machine-Learning for Knowledge Discovery and Adaptive Information Retrieval
Identifieur interne : 003546 ( Hal/Corpus ); précédent : 003545; suivant : 003547Neural-Symbolic Machine-Learning for Knowledge Discovery and Adaptive Information Retrieval
Auteurs : Hager Kammoun ; Jean-Charles Lamirel ; Mohammed Ben AhmedSource :
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Abstract
In this paper, a model for an information retrieval system is proposed which takes into account that knowledge about documents and information need of users are dynamic. Two methods are combined, on qualitative or symbolic and the other quantitative or numeric, which are deemed suitable for many clustering contexts, data analysis, concept exploring and knowledge discovery. These two methods may be classified as inductive learning techniques. In this model, they are introduced to build “long term” knowledge about past queries and concepts in a collection of documents. The “long term” knowledge can guide and assist the user to formulate an initial query and can be exploited in the process of retrieving relevant information. The different kinds of knowledge are organized in different points of view. This may be considered an enrichment of the exploration level which is coherent with the concept of document/query structure.
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Two methods are combined, on qualitative or symbolic and the other quantitative or numeric, which are deemed suitable for many clustering contexts, data analysis, concept exploring and knowledge discovery. These two methods may be classified as inductive learning techniques. In this model, they are introduced to build “long term” knowledge about past queries and concepts in a collection of documents. The “long term” knowledge can guide and assist the user to formulate an initial query and can be exploited in the process of retrieving relevant information. The different kinds of knowledge are organized in different points of view. This may be considered an enrichment of the exploration level which is coherent with the concept of document/query structure.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Neural-Symbolic Machine-Learning for Knowledge Discovery and Adaptive Information Retrieval</title><author role="aut"><persName><forename type="first">Hager</forename><surname>Kammoun</surname></persName><email></email><idno type="halauthor">135949</idno></author><author role="aut"><persName><forename type="first">Jean-Charles</forename><surname>Lamirel</surname></persName><email>lamirel@loria.fr</email><idno type="idhal">jean-charles-lamirel</idno><idno type="halauthor">71068</idno><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">Mohammed</forename><surname>Ben Ahmed</surname></persName><email></email><idno type="halauthor">135958</idno></author><editor role="depositor"><persName><forename>Jean-Charles</forename><surname>Lamirel</surname></persName><email>lamirel@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-10-08 17:17:21</date><date type="whenModified">2016-05-19 01:09:10</date><date type="whenReleased">2006-10-08 17:17:21</date><date type="whenProduced">2005</date></edition><respStmt><resp>contributor</resp><name key="107460"><persName><forename>Jean-Charles</forename><surname>Lamirel</surname></persName><email>lamirel@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-00104630</idno><idno type="halUri">https://hal.archives-ouvertes.fr/hal-00104630</idno><idno type="halBibtex">kammoun:hal-00104630</idno><idno type="halRefHtml">2005, Word Enformatika Society - 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ISSN 1305-5313</publisher><biblScope unit="pp">295--299</biblScope><date type="datePub">2005</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Self Organizing Map</term><term xml:lang="en">Information Retrieval Systems</term><term xml:lang="en">machine learning</term><term xml:lang="en">classification</term><term xml:lang="en">Galois lattices</term></keywords><classCode scheme="halDomain" n="info.info-lg">Computer Science [cs]/Machine Learning [cs.LG]</classCode><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halDomain" n="info.info-ir">Computer Science [cs]/Information Retrieval [cs.IR]</classCode><classCode scheme="halDomain" n="info.info-tt">Computer Science [cs]/Document and Text Processing</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">In this paper, a model for an information retrieval system is proposed which takes into account that knowledge about documents and information need of users are dynamic. Two methods are combined, on qualitative or symbolic and the other quantitative or numeric, which are deemed suitable for many clustering contexts, data analysis, concept exploring and knowledge discovery. These two methods may be classified as inductive learning techniques. In this model, they are introduced to build “long term” knowledge about past queries and concepts in a collection of documents. The “long term” knowledge can guide and assist the user to formulate an initial query and can be exploited in the process of retrieving relevant information. The different kinds of knowledge are organized in different points of view. This may be considered an enrichment of the exploration level which is coherent with the concept of document/query structure.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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