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Building linked ontologies with high precision using subclass mapping discovery

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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.

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Authors and Affiliations

  1. ADVIS Laboratory, Department of Computer Science, University of Illinois at Chicago, Chicago, Illinois, USA

    Isabel F. Cruz, Federico Caimi & Cosmin Stroe

  2. Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy

    Matteo Palmonari

Authors
  1. Isabel F. Cruz
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  2. Matteo Palmonari
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  3. Federico Caimi
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  4. Cosmin Stroe
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Corresponding author

Correspondence to Isabel F. Cruz.

Additional information

Research partially supported by NSF Awards IIS-0812258, IIS-1143926, and IIS-1213013 and by the Intelligence Advanced Research Projects Activity (IARPA) via Air Force Research Laboratory (AFRL) contract number 2010-04569-00-02. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, AFRL, or the U.S. Government.

Matteo Palmonari: Work partially performed while visiting the ADVIS Laboratory at UIC

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Cruz, I.F., Palmonari, M., Caimi, F. et al. Building linked ontologies with high precision using subclass mapping discovery. Artif Intell Rev 40, 127–145 (2013). https://doi.org/10.1007/s10462-012-9363-x

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