Supervised Learning of an Ontology Alignment Process
Identifieur interne : 001631 ( Main/Exploration ); précédent : 001630; suivant : 001632Supervised Learning of an Ontology Alignment Process
Auteurs : Marc Ehrig [Allemagne] ; York Sure [Allemagne] ; Steffen StaabSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: Ontology alignment is a crucial task to enable interoperability among different agents. However, the complexity of the alignment task especially for large ontologies requires automated support for the creation of alignment methods. When looking at current ontology alignment methods one can see that they are either not optimized for given ontologies or their optimization by machine learning means is mostly restricted to the extensional definition of ontologies. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. APFEL compares favorably in our experiments to competing approaches.
Url:
DOI: 10.1007/11590019_58
Affiliations:
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However, the complexity of the alignment task especially for large ontologies requires automated support for the creation of alignment methods. When looking at current ontology alignment methods one can see that they are either not optimized for given ontologies or their optimization by machine learning means is mostly restricted to the extensional definition of ontologies. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. APFEL compares favorably in our experiments to competing approaches.</div></front></TEI><affiliations><list><country><li>Allemagne</li></country><region><li>Bade-Wurtemberg</li><li>District de Karlsruhe</li></region><settlement><li>Karlsruhe</li></settlement></list><tree><noCountry><name sortKey="Staab, Steffen" sort="Staab, Steffen" uniqKey="Staab S" first="Steffen" last="Staab">Steffen Staab</name></noCountry><country name="Allemagne"><region name="Bade-Wurtemberg"><name sortKey="Ehrig, Marc" sort="Ehrig, Marc" uniqKey="Ehrig M" first="Marc" last="Ehrig">Marc Ehrig</name></region><name sortKey="Sure, York" sort="Sure, York" uniqKey="Sure Y" first="York" last="Sure">York Sure</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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