Simulated Annealing-based Ontology Matching

Abstract

Ontology alignment is a fundamental task to reconcile the heterogeneity among various information systems using distinct information sources. The evolutionary algorithms (EAs) have been already considered as the primary strategy to develop an ontology alignment system. However, such systems have two significant drawbacks: they either need a ground truth that is often unavailable, or they utilize the population-based EAs in a way that they require massive computation and memory. This article presents a new ontology alignment system, called SANOM, which uses the well-known simulated annealing as the principal technique to find the mappings between two given ontologies while no ground truth is available. In contrast to population-based EAs, the simulated annealing need not generate populations, which makes it significantly swift and memory-efficient for the ontology alignment problem. This article models the ontology alignment problem as optimizing the fitness of a state whose optimum is obtained by using the simulated annealing. A complex fitness function is developed that takes advantage of various similarity metrics including string, linguistic, and structural similarities. A randomized warm initialization is specially tailored for the simulated annealing to expedite its convergence. The experiments illustrate that SANOM is competitive with the state-of-the-art and is significantly superior to other EA-based systems.