MapPSO results for OAEI 2010

This paper presents and discusses the results produced by the MapPSO system for the 2010 Ontology Alignment Evaluation Initiative (OAEI). MapPSO is an ontology alignment approach based on discrete particle swarm optimisation (DPSO). Firstly, specific characteristics of the MapPSO system and their relation to the results obtained in the OAEI are discussed. Secondly, the results for the benchmarks and directory tracks are presented and discussed. 1 Presentation of the system With the 2008 OAEI campaign the MapPSO system (Ontology Mapping by Particle Swarm Optimisation) was introduced [1] as a novel approach to tackle the ontology alignment problem by applying the technique of particle swarm optimisation (PSO). 1.1 State, purpose, general statement The development of the MapPSO algorithm has been motivated by the following observations: 1. Ontologies are becoming numerous in number and large in size. 2. Ontologies evolve gradually. 3. Ontologies differ in key characteristics that can be exploited in order to compute alignments. Solving the ontology alignment problem using a PSO-based approach, as done by the MapPSO system, tackles these observations as follows: 1. PSO works inherently parallel, such that large ontologies can be aligned on a parallel computation infrastructure. 2. PSO works incrementally, which allows the algorithm to start with an initial or partial configuration (i.e. for instance an alignment of previous ontology versions) and refine it as the ontologies evolve. 3. PSO works as a meta-heuristic, i.e. independently of the objective function to be optimised. In the case of ontology alignment this means that the objective function can be adjusted according the particular alignment scenario at hand. The idea of the MapPSO approach is to provide an algorithm that fulfils the aforementioned characteristics. Particularly the focus is not to provide a universal library of similarity measures (base matchers) to form that specific objective function to be optimised, but rather to provide a scalable mechanism that can used with various objective functions depending on the alignment scenario at hand. MapPSO is still in the status of a research prototype, where recent work has been done exploiting the parallel nature of the algorithm in a cloud-based infrastructure [2]. 1.2 Specific techniques used MapPSO treats the ontology alignment problem as an optimisation problem and solves it by applying a discrete particle swarm optimisation (DPSO) algorithm [3]. To this end, each particle in the swarm represents a valid candidate alignment, which is updated in an iterative fashion. In each iteration, knowing about the particle representing the best alignment in the swarm, other particles adjust their alignments, influenced by this best particle. A random component when adjusting an alignment makes sure that the swarm does not converge to a