Computational intelligence approaches to ontology alignment

OASToday, I have given the Information Systems Colloquium for the IS@IEIS and IS@W&I groups of the Eindhoven University of Technology. The presentation covered the application of multi-objective evolutionary methods to certain aspects of the ontology alignment problem in semantic information systems. In the discussion that followed, we have also explored the links of the problem to business process discovery and alignment.

Achieving semantic interoperability is an essential task for all distributed and open knowledge based systems. Currently, the technology recognized for fulfilling this complex task is represented by ontologies. However, the power of ontological representation is reduced by the semantic heterogeneity problem which affects two ontologies when they are characterized by terminological and conceptual discrepancies. The most solid solution to overcome this problem is to perform an ontology alignment process capable of leading two heterogeneous ontologies into a mutual agreement by detecting a set of correspondences between them. Performing this task is an essential step to allow the exchange of information between people, organizations and web applications using ontologies for representing their view of the world. In this presentation, we consider several computational intelligence approaches to ontology alignment. In particular, the use of memetic algorithms, evolutionary approaches and fuzzy set methods are discussed for tackling different aspects of the problem.