A Unified Ontology Merging and Enrichment Framework

Abstract

With the growing development of heterogeneous domain-specific ontologies, the treatment of the semantic and structural differences between such ontologies becomes more important. In addition, constant maintenance and update is required so that they can be promptly enriched with new concepts and instances. In this paper, we present a coupled statistical/semantic framework for ontology merging and enrichment. First, we prioritize the ontology merging techniques according to their significance and execution into semantic-based, name-based, and statistical-based techniques respectively. In addition, we exploit multiple knowledge bases to support the merging task. Second, we use the massive amount of information encoded in texts on the Web as a corpus to enrich the merged ontology. An experimental instantiation of the framework and comparisons with state-of-the-art syntactic and semantic-based merging and enrichment systems validate our proposal.