Reconciling Heterogeneous Knowledge with Ontology Matching

Abstract

In open, dynamic and distributed systems, it is unrealistic to assume that autonomous agents or peers are committed to a common way of expressing their knowledge, in terms of one or more ontologies modelling the domain of interest. Thus, before any kind of communication or cooperation, agents must reach an agreement on the meaning of the terms they use for structuring information, conceptualizing the world, or representing distinct entities. Over the years several approaches have been proposed for semantic agreement driven by ontology matching in a distributed setting: argumentation-based models, constraint satisfaction methods and probabilistic models. The aim of this chapter is to present a brief overview of the state-of-the-art on these approaches and discuss the main open issues and challenges for future research. We firstly introduce the ontology matching process for semantic agreements and the notion of argumentation frameworks, and then we present scenarios applying such frameworks. Next, we specify the problem of synthesizing different matching methods as a constraint optimization problem and show the benefits of this approach and we present an approach for peers organized in arbitrary networks to reach semantic agreement on their correspondences. Finally, we discuss some open issues and future research directions for semantic agreement based on ontology matching.