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Communication Dans Un Congrès Année : 2019

Partitioning and Local Matching Learning of Large Biomedical Ontologies

Résumé

Conventional ontology matching systems are not well-tailored to ensure sufficient quality alignments for large ontology matching tasks. In this paper, we propose a local matching learning strategy to align large and complex biomedical ontologies. We define a novel partitioning approach that breakups large ontology alignment task into a set of local sub-matching tasks. We perform a machine learning approach for each local sub-matching task. We build a local machine learning model for each sub-matching task without any user involvement. Each local matching learning model automatically provides adequate matching settings for each local sub-matching task. Our results show that: (i) partitioning approach outperforms existing techniques, (ii) local matching while using a specific machine learning model for each sub-matching task yields to promising results and (iii) the combination between partitioning and machine learning increases the overall result.
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Dates et versions

hal-03621640 , version 1 (28-03-2022)

Identifiants

  • HAL Id : hal-03621640 , version 1
  • OATAO : 24734

Citer

Amir Laadhar, Faiza Ghozzi, Imen Megdiche, Franck Ravat, Olivier Teste, et al.. Partitioning and Local Matching Learning of Large Biomedical Ontologies. 34th ACM/SIGAPP Symposium on Applied Computing (SAC 2019), ACM SIGAPP: Special Interest Group on Applied Computing, Apr 2019, Limassol, Cyprus. pp.2285-2292. ⟨hal-03621640⟩
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