Selection and Combination of Heterogeneous Mappings to Enhance Biomedical Ontology Matching

Amina Annane 1, 2 Zohra Bellahsene 2 Faiçal Azouaou 1 Clement Jonquet 3
2 FADO - Fuzziness, Alignments, Data & Ontologies
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 SMILE - Système Multi-agent, Interaction, Langage, Evolution
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : This paper presents a novel background knowledge approach which selects and combines existing mappings from a given biomedical ontology repository to improve ontology alignment. Current background knowledge approaches usually select either manually or automatically a limited number of different ontologies and use them as a whole for background knowledge. Whereas in our approach, we propose to pick up only relevant concepts and relevant existing mappings linking these concepts all together in a specific and customized background knowledge graph. Paths within this graph will help to discover new mappings. We have implemented and evaluated our approach using the content of the NCBO BioPortal repository and the Anatomy benchmark from the Ontology Alignment Evaluation Initiative. We used the mapping gain measure to assess how much our final background knowledge graph improves results of state-of-the-art alignment systems. Furthermore, the evaluation shows that our approach produces a high quality alignment and discovers map-pings that have not been found by state-of-the-art systems.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01395883
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Amina Annane, Zohra Bellahsene, Faiçal Azouaou, Clement Jonquet. Selection and Combination of Heterogeneous Mappings to Enhance Biomedical Ontology Matching. EKAW: Knowledge Engineering and Knowledge Management, Nov 2016, Bologne, Italy. pp.19-33, ⟨10.1007/978-3-319-49004-5_2⟩. ⟨lirmm-01395883⟩

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