Improving agent interoperability through a memetic ontology alignment: A comparative study

Interoperability is a key problem in agent-based systems where different interacting computational entities negotiate to achieve a common goal. In last years, this interoperability issue has been faced by exploiting the concept of ontology that enables a single agent to model its knowledge by means of a semantic description of a domain of interest. However, ontology ability to enable a full interoperability can be limited by the so-called semantic heterogeneity problem which arises when some discrepancies exist among ontologies modeling the knowledge related to different agents. As consequence, in order to enable an effective knowledge exchange, an ontology alignment process is necessary to lead proprietary ontologies to a mutual agreement. Recently, some studies have successfully investigated the suitability of memetic algorithms to solve this complex task. However, memetic algorithms are influenced by some design issues arising from the different choices that can be taken to implement them. The aim of this paper is to compare the performances yielded by different memetic ontology alignment systems in order to individuate the most suitable hybrid evolutionary approach which enables a strong agent interoperability. The comparison among the considered approaches is performed by applying a statistical multiple comparison procedure on a collection of ontologies belonging to the well-known Ontology Alignment Evaluation Initiative (OAEI) benchmarks.