Using Competitive Binary Particle Swarm Optimization Algorithm for Matching Sensor Ontologies

Abstract

Developing sensor ontologies and using them to annotate the sensor data is a feasible way to address the data heterogeneity issue on Internet of Things (IoT). However, the heterogeneity issue exists between different sensor ontologies hampers their communications. Sensor ontology matching aims at finding all the heterogeneous entities in two ontologies, which is a feasible solution for aggregating heterogeneous sensor ontologies. This work investigates swarm intelligence (SI)-based sensor ontology matching techniques and further proposes a competitive binary particle swarm optimization algorithm (CBPSO)-based sensor ontology matching technique. In particular, a guiding matrix (GM) is proposed to ensure the population’s diversity and a competitive evolutionary framework is presented. The experiment uses ontology alignment evaluation initiative (OAEI)’s benchmark and three real sensor ontologies to test CBPSO’s performance. The experimental results show that the competitive evolutionary framework is able to help CBPSO effectively optimize the alignment’s quality, and it significantly outperforms other SIs at 5% significant level.