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|Year=2005 | |Year=2005 | ||
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|Booktitle=Professional Knowledge Management: Third Biennial Conference, WM 2005 | |Booktitle=Professional Knowledge Management: Third Biennial Conference, WM 2005 | ||
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|Pages=508 - 517 | |Pages=508 - 517 | ||
|Publisher=Springer | |Publisher=Springer | ||
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+ | |Editor=Klaus-Dieter Althoff and Andreas Dengel and Ralph Bergmann and Markus Nick and Thomas Roth-Berghofer | ||
|Series=LNAI | |Series=LNAI | ||
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|Download=2005_882_Ehrig_Supervised_Lear_1.pdf | |Download=2005_882_Ehrig_Supervised_Lear_1.pdf | ||
|Link=http://dx.doi.org/10.1007/11590019_58 | |Link=http://dx.doi.org/10.1007/11590019_58 | ||
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Aktuelle Version vom 28. August 2018, 13:46 Uhr
Supervised Learning of an Ontology Alignment Process
Supervised Learning of an Ontology Alignment Process
Published: 2005
April
Herausgeber: Klaus-Dieter Althoff and Andreas Dengel and Ralph Bergmann and Markus Nick and Thomas Roth-Berghofer
Buchtitel: Professional Knowledge Management: Third Biennial Conference, WM 2005
Ausgabe: 3782
Reihe: LNAI
Seiten: 508 - 517
Verlag: Springer
Erscheinungsort: Kaiserslautern, Germany
Referierte Veröffentlichung
BibTeX
Kurzfassung
Semantic alignment between ontologies is a necessary precondition to establish interoperability
between agents or services using different ontologies. Thus, in recent years
different methods for automatic ontology alignment have been proposed to deal with
this challenge. Thereby, the proposed methods were constricted to one of two different
paradigms: Either, (i), proposals would include a manually predefined automatic
method for proposing alignments, which would be used in the actual alignment process. They typically consist of a number of substrategies such as finding similar
labels. Or, (ii), proposals would learn an automatic alignment method based on instance
representations, e.g. bag-of-word models of documents. Both paradigms suffer
from drawbacks. The first paradigm suffers from the problem that it is impossible,
even for an expert knowledge engineer, to predict what strategy of aligning entities
is most successful for a given pair of ontologies. This is especially the case with increasing
complexity of ontology languages or increasing amounts of domain specific
conventions. The second paradigm is often hurt by the lack of instances or instance
descriptions. Also, knowledge encoded in the intensional descriptions of concepts and
relations is only marginally exploited by this way.
Hence, there remains the need to automatically combine multiple diverse and complementary
alignment strategies of all indicators, i.e. extensional and intensional descriptions,
in order to produce comprehensive, effective and efficient semi-automatic
alignment methods. Such methods need to be flexible to cope with different strategies
for various application scenarios, e.g. by using parameters. We call them “Parameterizable
Alignment Methods” (PAM).We have developed a bootstrapping approach
for acquiring the parameters that drive such a PAM. We call our approach APFEL for
“Alignment Process Feature Estimation and Learning”.
ISBN: 3540304657
VG Wort-Seiten: 16
Download: Media:2005_882_Ehrig_Supervised_Lear_1.pdf
Weitere Informationen unter: Link
Maschinelles Lernen, Ontology Learning, Künstliche Intelligenz, Semantic Web