K.: One size does not fit all: Customizing ontology alignment using user feedback

Abstract. A key problem in ontology alignment is that different ontological fea-tures (e.g., lexical, structural or semantic) vary widely in their importance for different ontology comparisons. In this paper, we present a set of principled tech-niques that exploit user feedback to customize the alignment process for a given pair of ontologies. Specifically, we propose an iterative supervised-learning ap-proach to (i) determine the weights assigned to each alignment strategy and use these weights to combine them for matching ontology entities; and (ii) determine the degree to which the information from such matches should be propagated to their neighbors along different relationships for collective matching. We demon-strate the utility of these techniques with standard benchmark datasets and large, real-world ontologies, showing improvements in F-scores of up to 70 % from the weighting mechanism and up to 40 % from collective matching, compared to an unweighted linear combination of matching strategies without information prop-agation.