National audienceLarge biomedical ontologies such as SNOMED CT, NCI, and FMA are exten-sively employed in the biomedical domain. These complex ontologies are basedon diverse modelling views and vocabularies. We define an approach that breaksup  a  large  ontology  alignment  problem  into  a  set  of  smaller  matching  tasks.We coupled this approach with an automated tuning process, which generatesthe adequate thresholds of the available similarity measure for any biomedicalmatching  task.  Experiments  demonstrate  that  the  coupling  between  ontologypartitioning and threshold tuning outperforms the existing approaches

Ghozzi, Faiza

Ichise, Ryutaro

Laadhar, Amir

Megdiche Bousarsar, Imen

Ravat, Franck

Teste, Olivier

Megdiche, Imen

Scientific Publications of the University of Toulouse II Le Mirail

Partitioning and Matching Tuning of Large Biomedical Ontologies

Archive Ouverte en Sciences de l'Information et de la Communication

Large biomedical ontologies such as SNOMED CT, NCI, and FMA are exten-sively employed in the biomedical domain. These complex ontologies are basedon diverse modelling views and vocabularies. We define an approach that breaksup  a  large  ontology  alignment  problem  into  a  set  of  smaller  matching  tasks.We coupled this approach with an automated tuning process, which generatesthe adequate thresholds of the available similarity measure for any biomedicalmatching  task.  Experiments  demonstrate  that  the  coupling  between  ontologypartitioning and threshold tuning outperforms the existing approaches

Open Archive Toulouse Archive Ouverte

Any correspondence concerning this service should be sent to the repository administrator: tech-oatao@listes-diff.inp-toulouse.fr This is an author’s version published in: http://oatao.univ-toulouse.fr/22528 Official URL  http://ceur-ws.org/Vol-2288/om2018_poster3.pdf Open  Archive  Toulouse  Archive  Ouverte OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: Laadhar, Amir and Ghozzi, Faiza and Ichise, Ryutaro and Megdiche-Bousarsar, Imen and Ravat, Franck and Teste, Olivier Partitioning and Matching Tuning of Large Biomedical Ontologies. (2018) In: 13th International Workshop on Ontology Matching co-located with the 17th International Semantic Web Conference (OM 2018), 8 October 2018 (Monterey, CA, United States). Partitioning and Matching Tuning of LargeBiomedical OntologiesAmir Laadhar1, Faiza Ghozzi2, Ryutaro Ichise3, Imen Megdiche1, FranckRavat1, and Olivier Teste11 Toulouse University, IRIT (CNRS/UMR 5505), Toulouse, France{firstname.lastname}@irit.fr2 University of Sfax, MIRACL, Sfax, Tunisiafaiza.ghozzi@isims.usf.tn3 National Institute of Informatics, Tokyo, Japanichise@nii.ac.jp1 IntroductionLarge biomedical ontologies such as SNOMED CT, NCI, and FMA are exten-sively employed in the biomedical domain. These complex ontologies are basedon diverse modelling views and vocabularies. We define an approach that breaksup a large ontology alignment problem into a set of smaller matching tasks.We coupled this approach with an automated tuning process, which generatesthe adequate thresholds of the available similarity measure for any biomedicalmatching task. Experiments demonstrate that the coupling between ontologypartitioning and threshold tuning outperforms the existing approaches.2 Partitioning and Matching Tuning of BiomedicalOntologies2.1 Architecture overviewIn figure 1, we depict the different stages for ontologies partitioning and thresholdtuning. These stages are detailed in the following sections.Fig. 1. Architecture Overview2.2 Ontologies PartitioningWe employ the hierarchical agglomerative clustering technique to divide an on-tology into a set of partitions. This method is based on the equation 1 to computethe structural similarity between the entities of the input ontologies. This equa-tion is inspired by Wu and Palmer [4] similarity measure. The partitioning ofevery ontology results in a dendrogram. We cut each dendrogram automaticallyin order to result in a set of partitions. We examine the output of all the possiblecuts until finding the first cut which do not result in any isolated partitions. Iso-lated partitions are partitions containing only one entity. We identify the similarpartition-pairs through the set of exact matchings between the input ontologies.StrcSim(ei,m, ei,n) =Dist(ri, lca)× 2Dist(ei,m, lca) + Dist(ei,n, lca) + Dist(ri, lca)× 2 (1)2.3 Threshold tuningThe available external knowledge sources represent mediator biomedical ontolo-gies between the two input ontologies. We cross-search the input ontologies andthe mediating ontology in order to find synthetic reference alignments. We com-pute the similarity score Sim between all the annotations of the generated align-ments. These similarity scores are represented by: simScore ={sim1,... ,simn}.The threshold Th value is deducted from simScore using the Equation 2:Th =∑simnsim1simi|simScore| (2)3 ExperimentsIn Table 1, we compare our proposed partitioning approach to the currentlyavailable partitioning strategies using two OAEI 2017 biomedical data sets: theAnatomy task and the LargeBio small segments tasks.Table 1. Anatomy track partitioning resultsPrecision F-Measure Recall Number of partitionsProposed approach 0.945 0.883 0.829 57/57SeeCOnt [3] 0.951 0.863 0.789 NDFalcon [2] 0.964 0.730 0.591 139/119Alsayed et al. [1] 0.975 0.753 0.613 84/80We employed UBERON as an external biomedical knowledge for derivingsynthetic reference alignments. We use ISUB similarity measure to compute thesimilarity scores between the derived mappings. In Table 2, we illustrate theaccuracy of the partitioning approach with the deduced thresholds.Table 2. Accuracy and derived thresholds for Anatomy and LargeBio tracksPrecision F-Measure Recall Derived ThresholdAnatomy 0.945 0.883 0.829 0.91FMA-NCI 0.957 0.870 0.789 0.69FMA-SNOMED 0.860 0.674 0.554 0.75SNOMED-NCI 0.911 0.697 0.564 0.854 Conclusion and Future WorkAs future work, we intend to automate all the matching tuning process whilefocusing on different type of heterogeneity applied over the partitions-pairs.References1. Algergawy, Alsayed, Sabine Massmann, and Erhard Rahm. ”A clustering-based ap-proach for large-scale ontology matching.” East European Conference on Advancesin Databases and Information Systems. Springer, Berlin, Heidelberg, (2011).2. Hu, Wei, Yuzhong Qu, and Gong Cheng. ”Matching large ontologies: A divide-and-conquer approach.” Data Knowledge Engineering 67.1, (2008).3. Algergawy, Alsayed, Samira Babalou, Mohammad J. Kargar, and S. HashemDavarpanah. ”Seecont: A new seeding-based clustering approach for ontology match-ing.” In East European Conference on Advances in Databases and Information Sys-tems, Springer (2015).4. Wu, Zhibiao, and Martha Palmer. ”Verbs semantics and lexical selection.” In Pro-ceedings of the 32nd annual meeting on Association for Computational Linguistics,(1994).

http://oatao.univ-toulouse.fr/22528/1/laadhar_22528.pdf

Partitioning and Matching Tuning of Large Biomedical Ontologies

Abstract

Similar works

Full text

Available Versions

Scientific Publications of the University of Toulouse II Le Mirail

Archive Ouverte en Sciences de l'Information et de la Communication

Open Archive Toulouse Archive Ouverte