Partitioning and local matching learning of large biomedical ontologies

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.

[1]  J. Euzenat,et al.  Ontology Matching , 2007, Springer Berlin Heidelberg.

[2]  Heiner Stuckenschmidt,et al.  Results of the Ontology Alignment Evaluation Initiative 2007 , 2006, OM.

[3]  Zohra Bellahsene,et al.  Overview of YAM++ - (not) Yet Another Matcher for ontology alignment task , 2016, J. Web Semant..

[4]  Martin Porter,et al.  Snowball: A language for stemming algorithms , 2001 .

[5]  Ryutaro Ichise,et al.  Machine Learning Approach for Ontology Mapping Using Multiple Concept Similarity Measures , 2008, Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008).

[6]  Mohammad Javad Kargar,et al.  SeeCOnt: A New Seeding-Based Clustering Approach for Ontology Matching , 2015, ADBIS.

[7]  Olivier Bodenreider,et al.  The Unified Medical Language System (UMLS): integrating biomedical terminology , 2004, Nucleic Acids Res..

[8]  Yuzhong Qu,et al.  Matching large ontologies: A divide-and-conquer approach , 2008, Data Knowl. Eng..

[9]  Daniel Müllner,et al.  Modern hierarchical, agglomerative clustering algorithms , 2011, ArXiv.

[10]  Valerie V. Cross,et al.  On partitioning for ontology alignment , 2017, OM@ISWC.

[11]  Mathieu d'Aquin,et al.  Ontology Modularization for Knowledge Selection: Experiments and Evaluations , 2007, DEXA.

[12]  Emanuel Santos,et al.  The AgreementMakerLight Ontology Matching System , 2013, OTM Conferences.

[13]  Jeng-Shyang Pan,et al.  A segment-based approach for large-scale ontology matching , 2017, Knowledge and Information Systems.

[14]  Alireza Osareh,et al.  ONTOLOGY ALIGNMENT USING MACHINE LEARNING TECHNIQUES , 2011 .

[15]  Faïez Gargouri,et al.  POMap: An Effective Pairwise Ontology Matching System , 2017, KEOD.

[16]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[17]  C E Lipscomb,et al.  Medical Subject Headings (MeSH). , 2000, Bulletin of the Medical Library Association.

[18]  S. Lewis,et al.  Uberon, an integrative multi-species anatomy ontology , 2012, Genome Biology.

[19]  Erhard Rahm,et al.  A Clustering-Based Approach for Large-Scale Ontology Matching , 2011, ADBIS.

[20]  Valerie V. Cross,et al.  We divide, you conquer: from large-scale ontology alignment to manageable subtasks with a lexical index and neural embeddings , 2018, OM@ISWC.

[21]  Isabel F. Cruz,et al.  Tackling the challenges of matching biomedical ontologies , 2018, J. Biomed. Semant..

[22]  Patrick Lambrix,et al.  Reducing the search space in ontology alignment using clustering techniques and topic identification , 2015, K-CAP.

[23]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[24]  Erhard Rahm,et al.  GOMMA results for OAEI 2012 , 2012, OM.

[25]  Erhard Rahm,et al.  Schema and ontology matching with COMA++ , 2005, SIGMOD '05.

[26]  Heiner Stuckenschmidt,et al.  Improving Ontology Matching Using Meta-level Learning , 2009, ESWC.

[27]  Christopher G. Chute,et al.  BioPortal: ontologies and integrated data resources at the click of a mouse , 2009, Nucleic Acids Res..

[28]  Stefano Spaccapietra,et al.  Modular Ontologies: Concepts, Theories and Techniques for Knowledge Modularization , 2009, Modular Ontologies.

[29]  Olivier Teste,et al.  Partitioning and matching tuning of large biomedical ontologies , 2018, OM@ISWC.