Domain-Specific Ontology Mapping by Corpus-Based Semantic Similarity

Mapping heterogeneous ontologies is usually performed manually by domain experts, or accomplished by computer programs via comparing the structures of the ontologies and the linguistic semantics of their concepts. In this work, we take a different approach to compare and map the concepts of heterogeneous domain-specific ontologies by using a document corpus in a domain similar to the domain of the ontologies as a bridge. Cosine similarity and Jaccard coefficient, two vector-based similarity measures commonly used in the field of information retrieval are adopted to compare semantic similarity between ontologies. Additionally, the market basket model is modified as a relatedness analysis measure for ontology mapping. We use regulations as the bridging document corpus and the consideration of the corpus hierarchical information in concept similarity comparison. Preliminary results are obtained using ontologies from the architectural, engineering and construction (AEC) industry. The proposed market basket model appears to outperform the other two similarity measures, with its prediction error reduced using corpus structural information.