Discrete particle swarm optimisation for ontology alignment

Abstract

Particle Swarm Optimisation (PSO) is a biologically-inspired, population-based optimisation technique that has been successfully applied to various problems in science and engineering. In the context of semantic technologies, optimisation problems also occur but have rarely been considered as such. This work addresses the problem of ontology alignment, which is the identification of overlaps in heterogeneous knowledge bases backing semantic applications. To this end, the ontology alignment problem is revisited as an optimisation problem. A discrete particle swarm optimisation algorithm is designed in order to solve this optimisation problem and compute an alignment of two ontologies. A number of characteristics of traditional PSO algorithms are partially relaxed in this article, such as fixed dimensionality of particles. A complex fitness function based on similarity measures of ontological entities, as well as a tailored particle update procedure are presented. This approach brings several benefits for solving the ontology alignment problem, such as inherent parallelisation, anytime behaviour, and flexibility according to the characteristics of particular ontologies. The presented algorithm has been implemented under the name MapPSO (Ontology Mapping using Particle Swarm Optimisation). Experiments demonstrate that applying PSO in the context of ontology alignment is a feasible approach.

Set-based particle swarm optimization applied to the multidimensional knapsack problem

“…The algorithm proposed by Correa et al (2006) for attribute selection and the related algorithm by Bock and Hettenhausen (2012) for ontology alignment both have set-like characteristics, but both contain problem specific elements. Especially, the concept of a personal likelihood that requires each element in a particle position to have its own partial objective function value, prevents these algorithms from being applied to many discrete optimization problems, including the MKP.…”

Leveraging Ontologies for Environmental Information Systems

“…In the SUI project, we use a tool developed in the German Research Program THESEUS [21] which utilizes Particle Swarm Optimization (PSO) to search for the optimal alignment of ontologies [4]. It was specially designed for parallel execution in distributed systems and exhibits excellent performance especially for large ontologies.…”

Aggregated Similarity Optimization in Ontology Alignment through Multiobjective Particle Swarm Optimization

“…Over the last decade, many evolutionary based approaches have been implemented in [5], [6], [7] to optimize the quality of ontology alignment. But their design format is based on single objective optimization problem [8]. This reality motivated us to develop such an approach where multiple objectives are optimized in parallel.…”

“…G. Acampora et al [9] proposes a memetic algorithm to perform an automatic matching process capable of computing a sub-optimal alignment between two ontologies. In article [8], J. Bock et al applied discrete particle swarm optimization for ontology alignment. Holistic ontology alignment by population based optimization is depicted in [10].…”