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Fuzzy ontology alignment using background knowledge
Abstract We propose an ontology alignment frameworkWe propose an ontology alignment framework with two core features: the use of background knowledge and the ability to handle vagueness in the matching process and the resulting concept alignments. The procedure is based on the use of a generic reference vocabulary, which is used for fuzzifying the ontologies to be matched. The choice of this vocabulary is problem-dependent in general, although Wikipedia represents a general-purpose source of knowledge that can be used in many cases, and even allows cross language matchings. In the first step of our approach, each domain concept is represented as a fuzzy set of reference concepts. In the next step, the fuzzified domain concepts are matched to one another, resulting in fuzzy descriptions of the matches of the original concepts. Based on these concept matches, we propose an algorithm that produces a merged fuzzy ontology that captures what is common to the source ontologies. The paper describes experiments in the domain of multimedia by using ontologies containing tagged images, as well as an evaluation of the approach in an information retrieval setting. The undertaken fuzzy approach has been compared to a classical crisp alignment by the help of a ground truth that was created based on human judgment. that was created based on human judgment.
Abstractsub We propose an ontology alignment frameworkWe propose an ontology alignment framework with two core features: the use of background knowledge and the ability to handle vagueness in the matching process and the resulting concept alignments. The procedure is based on the use of a generic reference vocabulary, which is used for fuzzifying the ontologies to be matched. The choice of this vocabulary is problem-dependent in general, although Wikipedia represents a general-purpose source of knowledge that can be used in many cases, and even allows cross language matchings. In the first step of our approach, each domain concept is represented as a fuzzy set of reference concepts. In the next step, the fuzzified domain concepts are matched to one another, resulting in fuzzy descriptions of the matches of the original concepts. Based on these concept matches, we propose an algorithm that produces a merged fuzzy ontology that captures what is common to the source ontologies. The paper describes experiments in the domain of multimedia by using ontologies containing tagged images, as well as an evaluation of the approach in an information retrieval setting. The undertaken fuzzy approach has been compared to a classical crisp alignment by the help of a ground truth that was created based on human judgment. that was created based on human judgment.
Bibtextype article  +
Doi 10.1142/S0218488514500044  +
Has author Todorov K. + , Hudelot C. + , Adrian Popescu + , Geibel P. +
Has extra keyword Back-ground knowledge + , Concept alignments + , Fuzzy description + , Fuzzy ontology + , Matching process + , Ontology alignment + , Reference concepts + , Wikipedia + , Fuzzy sets + , Information retrieval + , Image matching +
Has keyword (fuzzy) ontology alignment + , Background knowledge + , Fuzzy ontologies + , Information retrieval + , Wikipedia +
Issn 2184885  +
Issue 1  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 75–112  +
Published in International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems +
Title Fuzzy ontology alignment using background knowledge +
Type journal article  +
Volume 22  +
Year 2014 +
Creation dateThis property is a special property in this wiki. 7 November 2014 18:25:04  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 18:25:04  +
DateThis property is a special property in this wiki. 2014  +
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