Multiple Kernel Learning for Ontology Instance Matching

Abstract

This paper proposes to apply Multiple Kernel Learning and Indefinite Kernels (IK) to combine and tune Similarity Measures within the context of Ontology Instance Matching. We explain why MKL can be used in parameter selection and similarity measure combination; argue that IK theory is required in order to use MKL within this context; propose a configuration that makes use of both concepts; and present, using the IIMB bechmark, results of a prototype to show the feasibility of this idea in comparison with other matching tools.