Affiliations: [a] Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Minhou, Fuzhou, Fujian, China
| [b] College of Information Science and Engineering, Fujian University of Technology, Minhou, Fuzhou, Fujian, China
| [c] Intelligent Information Processing Research Center, Fujian University of Technology, Minhou, Fuzhou, Fujian, China
| [d] Institute of Artificial Intelligence, Fujian University of Technology, Minhou, Fuzhou, Fujian, China
| [e] Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Minhou, Fuzhou, Fujian, China
Abstract: Biomedical ontology matching dedicates to find two heterogeneous ontologies’ alignment and address their heterogeneity problem. Typically, a biomedical ontology has various biomedical concepts that are described with various labels and datatype property names, which forms a lexical space where each label or datatype property represents one dimension. Therefore, it is an effective way to present two biomedical concepts in a vector space, and use the cosine distance to measure their similarity. In this work, we present two biomedical concepts in a lexical vector space which is constructed with their inner and context concepts’ lexical information, and then utilize two vector’s cosine distance to measure similarity value. Then, we propose a compact Evolutionary Algorithm (cEA) to find the concept correspondences. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s testing cases, and the expeirmental results with Vector space Based Ontology Matcher (VBOM), Genetic Algorithm based Ontology Matcher (GAOM) and OAEI’s participants show the effectiveness of our proposal.
