Ontology Alignment based on Instances using Hybrid Genetic Algorithm

Abstract

The popularity of Ontology favored the appearance of several Ontologies to the same domain, thereby increasing the need of alignment techniques. In scenarios where ontologies comprising instances, the knowledge embedded in these instances can be useful to improve alignment. This paper extends a hybrid evolutionary approach, which applies a local optimization method, by taking instances into account in order to reduce premature convergence and, consequently, improve the quality of the resulting ontology alignment.

Introduction

O2 elements # name 0 thing 1 transport 2 car 3 Volkswagen 4 Porsche 5 engine 6 speed 7 has motor 8 has property 9 Mark´s Porsche 10 motor 123456 11 fast Possible solution chromosome

(0,1,7,3,9,11,8,8,9,9,11) In order to take instances into account during the alignment problem, we propose an additional function. This function applies the concept of upPropagation [ Massmann et al. 2011 ], in which the similarities between instances are propagated to their concepts when evaluating a possible solution. Moreover, our approach will initially adopt specific values for the genetic parameters, following the work of Souza [2012 ]: a selection rate of 50%, crossover probability of 80%, mutation probability of 10%, a 30% rate for reinsertion of best individuals, a 10% rate for reinsertion of the worst individuals, mortality of 5 generations, a local search frequency of every 100 generations and, finally, a 25% insertion neighborhood. By adopting this parameter value set, avoid solutions that persist for many generations, like super individuals or solutions very bad, is applied the concept of mortality in the population. Individuals that reach a certain age m are dropped from the new generation. Finally, we assume the existence of a reference alignment and predefined thresholds for precision, recall and F-measures as our stopping criteria. 3

Conclusion

Typically, ontologies are used by people, artificial agents and distributed applications that need to share domain information about a specific subject or area of knowledge. However, the creation of these ontologies is commonly performed in accordance with local needs and often without concern for reuse. In an ever-increasing frequent scenario where various ontologies for the same domain exist, alignment of them is a must, but still remains as a challenging problem. In many of these scenarios, instances may potentially bring extra information helping the alignment process, but are currently under-exploited in the literature, especially when combined with other approaches. In this paper, we propose to use instances to improve the alignment of ontologies through the use of a genetic algorithm combined with a local search heuristic to reduce premature convergence. Experiments are being performed to evaluate our proposal.

References

Acampora , G. , Loia , V. , Salerno , S. and Vitiello , A. ( 2012 ), “A hybrid evolutionary approach for solving the ontology alignment problem” , In: International Journal of Intelligent Systems , 27 : 189 - 216 . doi: 10 .1002/int.20517.

Euzenat , J. and Shvaiko , P. ( 2007 ), “Ontology Matching”, Springer-Verlag, Berlin Heidelberg. 2007 , X, 334 p. 67 illus . ISBN 978-3-540-49611-3.

Massmann , S. ; Raunich, Salvatore; Aumueller, David; Arnold, Patrick; Rahm e Erhard. ( 2011 ) ” Evolution of the COMA Match System” , OM-2011 (The Sixth International Workshop on Ontology Matching , October 24th , 2011 , Bonn, Germany).

Souza , Jairo Francisco. ( 2012 ) “A heuristic approach single-objective for calibration in meta-ontology aligners” . Rio de Janeiro, 105p. PhD Thesis , Department of Computer Science, Pontifícia Universidade Católica do Rio de Janeiro.