Discrete particle swarm optimisation for ontology alignment

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.

[1]  Heiko Paulheim.  On Applying Matching Tools to Large-scale Ontologies , 2008, OM.

[2]  Enrico Motta,et al.  Proceedings of the 8th International Semantic Web Conference , 2009 .

[3]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[4]  J. Euzenat,et al.  Ontology Matching , 2007, Springer Berlin Heidelberg.

[5]  Alex Alves Freitas,et al.  Particle swarm and bayesian networks applied to attribute selection for protein functional classification , 2007, GECCO '07.

[6]  Natalya F. Noy.  Using Non-Local Context for Semantic Matching , 2001, IJCAI 2001.

[7]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[8]  WangShan,et al.  Graph-based query rewriting for knowledge sharing between peer ontologies , 2008 .

[9]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[10]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[11]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[12]  Mark A. Musen,et al.  Anchor-PROMPT: Using Non-Local Context for Semantic Matching , 2001, OIS@IJCAI.

[13]  Yaowu Wu,et al.  A PSO-based approach to optimal capacitor placement with harmonic distortion consideration , 2004 .

[14]  Rami Bahsoon,et al.  Interschema correspondence establishment in a cooperative OWL-based multi-information server grid environment , 2008, Inf. Sci..

[15]  Jérôme Euzenat,et al.  Similarity-Based Ontology Alignment in OWL-Lite , 2004, ECAI.

[16]  Alex Alves Freitas,et al.  A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set , 2006, GECCO.

[17]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[18]  Marc Ehrig,et al.  Relaxed Precision and Recall for Ontology Matching , 2005, Integrating Ontologies.

[19]  Peter F. Patel-Schneider,et al.  OWL 2 Web Ontology Language Primer (Second Edition) , 2012 .

[20]  Xiaoyong Du,et al.  Graph-based query rewriting for knowledge sharing between peer ontologies , 2008, Inf. Sci..

[21]  Jan Hettenhausen.  RESEARCH ARTICLE Interactive Multi-Objective Particle Swarm Optimisation with Heatmap Visualisation based User Interface , 2008 .

[22]  Jérôme Euzenat,et al.  An API for Ontology Alignment , 2004, SEMWEB.

[23]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[24]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[25]  Jianguo Jiang,et al.  Searching for overlapping coalitions in multiple virtual organizations , 2010, Inf. Sci..

[26]  Prashant Doshi,et al.  Inexact Matching of Ontology Graphs Using Expectation-Maximization , 2006, AAAI.

[27]  Changjun Jiang,et al.  GAOM: Genetic Algorithm Based Ontology Matching , 2006, 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06).

[28]  Andrew Lewis,et al.  Interactive multi-objective particle swarm optimization with heatmap-visualization-based user interface , 2010 .

[29]  Enrico Motta,et al.  DSSim Results for OAEI 2008 , 2008, OM.

[30]  Stefanos D. Kollias,et al.  A String Metric for Ontology Alignment , 2005, SEMWEB.

[31]  Guilin Qi,et al.  Combination of Similarity Measures in Ontology Matching Using the OWA Operator , 2011, Recent Developments in the Ordered Weighted Averaging Operators.

[32]  Enrique Alba,et al.  Optimizing Ontology Alignments by Using Genetic Algorithms , 2008, NatuReS.

[33]  Eyal Oren,et al.  Anytime Query Answering in RDF through Evolutionary Algorithms , 2008, SEMWEB.

[34]  Jason J. Jung.  Reusing ontology mappings for query routing in semantic peer-to-peer environment , 2010, Inf. Sci..

[35]  Nicola Guarino,et al.  Formal ontology, conceptual analysis and knowledge representation , 1995, Int. J. Hum. Comput. Stud..