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A statistically-based ontology matching tool

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Abstract

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated development of large independent ontologies within the same or different domains with some overlapping information among them. In order to match such large ontologies, automatic matchers become an inevitable solution. This work explores the use of a predictive statistical model to establish an alignment between two input ontologies. We demonstrate how to integrate ontology partitioning and parallelism in the ontology matching process in order to make the statistical predictive model scalable to large ontology matching tasks. Unlike most ontology matching tools which establish 1:1 cardinality mappings, our statistical model generates one-to-many cardinality mappings.

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Notes

  1. http://disease-ontology.org/.

  2. https://www.nlm.nih.gov/mesh/.

  3. https://www.nlm.nih.gov/research/umls/sourcereleasedocs/index.html.

  4. http://www.lirmm.fr/yam-plus-plus/Yam++2012.zip.

  5. https://github.com/AgreementMakerLight/AML-Jar.

  6. https://github.com/ernestojimenezruiz/logmap-matcher.

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Authors and Affiliations

  1. Makerere University, Kampala, Uganda

    Peter Ochieng & Swaib Kyanda

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  1. Peter Ochieng
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  2. Swaib Kyanda
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Correspondence to Peter Ochieng.

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Ochieng, P., Kyanda, S. A statistically-based ontology matching tool. Distrib Parallel Databases 36, 195–217 (2018). https://doi.org/10.1007/s10619-017-7206-0

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