Improving Case Retrieval Using Typicality
Identifieur interne : 000284 ( Hal/Checkpoint ); précédent : 000283; suivant : 000285Improving Case Retrieval Using Typicality
Auteurs : Emmanuelle Gaillard [France] ; Jean Lieber [France] ; Emmanuel Nauer [France]Source :
English descriptors
Abstract
This paper shows how typicality can be used to improve the case retrieval of a case-based reasoning (CBR) system, improving at the same time the global results of the CBR system. Typicality discriminates subclasses of a class in the domain ontology depending of how a subclass is a good example for its class. Our approach proposes to partition the subclasses of some classes into atypical, normal and typical subclasses in order to refine the domain ontology. The refined ontology allows a finer-grained generalization of the query during the retrieval process. The benefits of this approach are presented according to an evaluation in the context of Taaable, a \cbr system designed for the cooking domain.
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Hal:hal-01178317Le document en format XML
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CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Typicality</term><term>case retrieval</term><term>cooking</term><term>ontology refinement</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper shows how typicality can be used to improve the case retrieval of a case-based reasoning (CBR) system, improving at the same time the global results of the CBR system. Typicality discriminates subclasses of a class in the domain ontology depending of how a subclass is a good example for its class. Our approach proposes to partition the subclasses of some classes into atypical, normal and typical subclasses in order to refine the domain ontology. The refined ontology allows a finer-grained generalization of the query during the retrieval process. The benefits of this approach are presented according to an evaluation in the context of Taaable, a \cbr system designed for the cooking domain.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Improving Case Retrieval Using Typicality</title><author role="aut"><persName><forename type="first">Emmanuelle</forename><surname>Gaillard</surname></persName><email></email><idno type="halauthor">664684</idno><affiliation ref="#struct-205125"></affiliation><affiliation ref="#struct-413289"></affiliation></author><author role="aut"><persName><forename type="first">Jean</forename><surname>Lieber</surname></persName><email></email><idno type="halauthor">59137</idno><affiliation ref="#struct-205125"></affiliation></author><author role="aut"><persName><forename type="first">Emmanuel</forename><surname>Nauer</surname></persName><email>Emmanuel.Nauer@loria.fr</email><idno type="halauthor">223663</idno><orgName ref="#struct-300289"></orgName><affiliation ref="#struct-205125"></affiliation></author><editor role="depositor"><persName><forename>Emmanuel</forename><surname>Nauer</surname></persName><email>Emmanuel.Nauer@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2015-07-18 17:26:42</date><date type="whenModified">2015-11-13 01:08:53</date><date type="whenReleased">2015-07-18 17:26:42</date><date type="whenProduced">2015-09-28</date></edition><respStmt><resp>contributor</resp><name key="103884"><persName><forename>Emmanuel</forename><surname>Nauer</surname></persName><email>Emmanuel.Nauer@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01178317</idno><idno type="halUri">https://hal.inria.fr/hal-01178317</idno><idno type="halBibtex">gaillard:hal-01178317</idno><idno type="halRefHtml">23rd International Conference on Case-Based Reasoning (ICCBR 2015), Sep 2015, Frankfurt am Main, Germany. 2015</idno><idno type="halRef">23rd International Conference on Case-Based Reasoning (ICCBR 2015), Sep 2015, Frankfurt am Main, Germany. 2015</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INRIA2">INRIA 2</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Improving Case Retrieval Using Typicality</title><author role="aut"><persName><forename type="first">Emmanuelle</forename><surname>Gaillard</surname></persName><idno type="halAuthorId">664684</idno><affiliation ref="#struct-205125"></affiliation><affiliation ref="#struct-413289"></affiliation></author><author role="aut"><persName><forename type="first">Jean</forename><surname>Lieber</surname></persName><idno type="halAuthorId">59137</idno><affiliation ref="#struct-205125"></affiliation></author><author role="aut"><persName><forename type="first">Emmanuel</forename><surname>Nauer</surname></persName><email>Emmanuel.Nauer@loria.fr</email><idno type="halAuthorId">223663</idno><orgName ref="#struct-300289"></orgName><affiliation ref="#struct-205125"></affiliation></author></analytic><monogr><meeting><title>23rd International Conference on Case-Based Reasoning (ICCBR 2015)</title><date type="start">2015-09-28</date><date type="end">2015-09-30</date><settlement>Frankfurt am Main</settlement><country key="DE">Germany</country></meeting><imprint><date type="datePub">2015</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Typicality</term><term xml:lang="en">ontology refinement</term><term xml:lang="en">case retrieval</term><term xml:lang="en">cooking</term></keywords><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">This paper shows how typicality can be used to improve the case retrieval of a case-based reasoning (CBR) system, improving at the same time the global results of the CBR system. Typicality discriminates subclasses of a class in the domain ontology depending of how a subclass is a good example for its class. Our approach proposes to partition the subclasses of some classes into atypical, normal and typical subclasses in order to refine the domain ontology. The refined ontology allows a finer-grained generalization of the query during the retrieval process. The benefits of this approach are presented according to an evaluation in the context of Taaable, a \cbr system designed for the cooking domain.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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