Ternary Compound Matching of Biomedical Ontologies with Compact Multi-Objective Evolutionary Algorithm Based on Adaptive Objective Space Decomposition

A biomedical ontology provides a formal definition on the concepts and their relationships in the biomedical domain, which supports applications such as biomedical data annotation, knowledge integration, search and analysis. Different biomedical ontologies are mostly developed independently, and thus, establishing meaningful links between their entities, so-called ontology matching, is critical to implement their inter-operation. Since biomedical research usually spans multiple domains and topics, which motivates a new type of complex ontology matching, i.e. compound ontology matching, which involves more than two ontologies. Due to the complexity of the ontology matching problem, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. However, there exist different aspects of a solution that are partially or wholly in conflict, and the single-objective EA may lead to unwanted bias to one of them and reduce the solution's quality. To improve the ternary compound alignment's quality when matching three biomedical ontologies, in this work, a compact Multi-Objective Evolutionary Algorithm Based On Adaptive Objective Space Decomposition (cMOEA-AOSD) based matching technique is proposed. In particular, a Ternary Compound Concept Similarity Measure (TCCSM) is proposed to calculate the similarity value of three biomedical concepts, a mathematical model for ternary compound matching problem is constructed, and a cMOEA-AOSD is presented to address it, which is able to adaptively decompose the objective space to ensure the diversity of the solutions in Pareto Front (PF) and the quality of the final solution. The experiment uses six testing cases that consists of nine biomedical ontologies to test our proposal's performance, and the experimental results show that cMOEA-AOSD significantly out performs other MOEA-based matching technique and the state-of-the-art ternary compound matching techniques.