Boosting the Reasoning-Based Approach by Applying Structural Metrics for Ontology Alignment
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초록
The amount of sources of information available on the web using ontologies as support continues to increaseand is often heterogeneous and distributed. Ontology alignment is the solution to ensure semantic interoperability. In this paper, we describe a new ontology alignment approach, which consists of combiningstructure-based and reasoning-based approaches in order to discover new semantic correspondences betweenentities of different ontologies. We used the biblio test of the benchmark series and anatomy series of theOntology Alignment Evaluation Initiative (OAEI) 2012 evaluation campaign to evaluate the performance ofour approach. We compared our approach successively with LogMap and YAM++ systems. We also analyzedthe contribution of our method compared to structural and semantic methods. The results obtained show thatour performance provides good performance. Indeed, these results are better than those of the LogMapsystem in terms of precision, recall, and F-measure. Our approach has also been proven to be more relevantthan YAM++ for certain types of ontologies and significantly improves the structure-based and reasoningbasedmethods.
Abstract
The amount of sources of information available on the web using ontologies as support continues to increaseand is often heterogeneous and distributed. Ontology alignment is the solution to ensure semantic interoperability. In this paper, we describe a new ontology alignment approach, which consists of combiningstructure-based and reasoning-based approaches in order to discover new semantic correspondences betweenentities of different ontologies. We used the biblio test of the benchmark series and anatomy series of theOntology Alignment Evaluation Initiative (OAEI) 2012 evaluation campaign to evaluate the performance ofour approach. We compared our approach successively with LogMap and YAM++ systems. We also analyzedthe contribution of our method compared to structural and semantic methods. The results obtained show thatour performance provides good performance. Indeed, these results are better than those of the LogMapsystem in terms of precision, recall, and F-measure. Our approach has also been proven to be more relevantthan YAM++ for certain types of ontologies and significantly improves the structure-based and reasoningbasedmethods.
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