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A Neural Model for Ontology Matching

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Executive Summary

Ontology matching is a key issue in the Semantic Web. The paper describes an unsupervised neural model for matching pairs of ontologies. The result of matching two ontologies is a class alignment, where each concept in one ontology is put into correspondence with a semantically related concept in the other one. The framework is based on a model of hierarchical self-organizing maps. Every concept of the two ontologies that are matched is encoded in a bag-of-words style, by counting the words that occur in their OWL concept definition. The authors evaluated this ontology matching model with the OAEI benchmark data set for the bibliography domain.

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