Building linked ontologies with high precision using subclass mapping discovery

Authors:
Isabel F. Cruz;Matteo Palmonari;Federico Caimi;Cosmin Stroe
Affiliations:
ADVIS Laboratory, Department of Computer Science, University of Illinois at Chicago, Chicago, USA;Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy;ADVIS Laboratory, Department of Computer Science, University of Illinois at Chicago, Chicago, USA;ADVIS Laboratory, Department of Computer Science, University of Illinois at Chicago, Chicago, USA
Venue:
Artificial Intelligence Review
Year:
2013

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Abstract

The creation of links between schemas of published datasets is a key part of the Linked Open Data (LOD) paradigm. The ability to discover these links "on the go" requires that ontology matching techniques achieve good precision and recall within acceptable execution times. In this paper, we add similarity-based and mediator-based ontology matching methods to the Agreementmaker ontology matching system, which aim to efficiently discover high precision subclass mappings between LOD ontologies. Similarity-based matching methods discover subclass mappings by extrapolating them from a set of high quality equivalence mappings and from the interpretation of compound concept names. Mediator-based matching methods discover subclass mappings by comparing polysemic lexical annotations of ontology concepts and by considering external web ontologies. Experiments show that when compared with a leading LOD approach, Agreementmaker achieves considerably higher precision and F-measure, at the cost of a slight decrease in recall.