Solving Ontology Metamatching Problem through Improved Multiobjective Particle Swarm Optimization Algorithm

Abstract

In recent years, knowledge representation in the Artificial Intelligence (AI) domain is able to help people understand the semantics of data and improve the interoperability between diverse knowledge-based applications. Semantic Web (SW), as one of the methods of knowledge representation, is the new generation of World Wide Web (WWW), which integrates AI with web techniques and dedicates to implementing the automatic cooperations among different intelligent applications. Ontology, as an information exchange model that defines concepts and formally describes the relationships between two concepts, is the core technique of SW, implementing semantic information sharing and data interoperability in the Internet of Things (IoT) domain. However, the heterogeneity issue hampers the communications among different ontologies and stops the cooperations among ontology-based intelligent applications. To solve this problem, it is vital to establish semantic relationships between heterogeneous ontologies, which is the so-called ontology matching. Ontology metamatching problem is commonly a complex optimization problem with many local optima. To this end, the ontology metamatching problem is defined as a multiobjective optimization model in this work, and a multiobjective particle swarm optimization (MOPSO) with diversity enhancing (DE) (MOPSO-DE) strategy is proposed to better trade off the convergence and diversity of the population. The well-known benchmark of the Ontology Alignment Evaluation Initiative (OAEI) is used in the experiment to test MOPSO-DE’s performance. Experimental results prove that MOPSO-DE can obtain the high-quality alignment and reduce the MOPSO’s memory consumption.