A Proposal for Combining Formal Concept Analysis and Description Logics for Mining Relational Data
Identifieur interne : 004D25 ( Main/Exploration ); précédent : 004D24; suivant : 004D26A Proposal for Combining Formal Concept Analysis and Description Logics for Mining Relational Data
Auteurs : Mohamed Hacene Rouane [Canada] ; Marianne Huchard ; Amedeo Napoli ; Petko Valtchev [Canada]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: Recent advances in data and knowledge engineering have emphasized the need for formal concept analysis (fca) tools taking into account structured data. There are a few adaptations of the classical fca methodology for handling contexts holding on complex data formats, e.g. graph-based or relational data. In this paper, relational concept analysis (rca) is proposed, as an adaptation of fca for analyzing objects described both by binary and relational attributes. The rca process takes as input a collection of contexts and of inter-context relations, and yields a set of lattices, one per context, whose concepts are linked by relations. Moreover, a way of representing the concepts and relations extracted with rca is proposed in the framework of a description logic. The rca process has been implemented within the Galicia platform, offering new and efficient tools for knowledge and software engineering.
Url:
DOI: 10.1007/978-3-540-70901-5_4
Affiliations:
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CV, Montréal, H3C 3J7</wicri:regionArea><wicri:noRegion>H3C 3J7</wicri:noRegion></affiliation><affiliation wicri:level="1"><country xml:lang="fr">Canada</country><wicri:regionArea>Dépt. informatique, UQAM, C.P. 8888, Succ. CV, Montréal, H3C 3P8</wicri:regionArea><wicri:noRegion>H3C 3P8</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="s" type="main" xml:lang="en">Lecture Notes in Computer Science</title><title level="s" type="abbrev">LNCS</title><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Recent advances in data and knowledge engineering have emphasized the need for formal concept analysis (fca) tools taking into account structured data. There are a few adaptations of the classical fca methodology for handling contexts holding on complex data formats, e.g. graph-based or relational data. In this paper, relational concept analysis (rca) is proposed, as an adaptation of fca for analyzing objects described both by binary and relational attributes. The rca process takes as input a collection of contexts and of inter-context relations, and yields a set of lattices, one per context, whose concepts are linked by relations. Moreover, a way of representing the concepts and relations extracted with rca is proposed in the framework of a description logic. 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