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Ondřej Šváb Vojtěch Svátek

Combining Ontology Mapping Methods Using Bayesian Networks Ontology Alignment Evaluation Initiative 2006 - 'Conference' Track. Ondřej Šváb Vojtěch Svátek. Overview. Ontology Mapping Combining Ontology Mapping Methods Using Bayesian Networks String distance metrics Mapping patterns OAEI

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Ondřej Šváb Vojtěch Svátek

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  1. Combining Ontology Mapping Methods Using Bayesian NetworksOntology Alignment Evaluation Initiative 2006 - 'Conference' Track Ondřej Šváb Vojtěch Svátek KEG seminar

  2. Overview • Ontology Mapping • Combining Ontology Mapping Methods • Using Bayesian Networks • String distance metrics • Mapping patterns • OAEI • Our track – conference domain • Evaluation KEG seminar

  3. Ontology Mapping Ontology Mapping = discovering of Semantic correspondencies (equivalence, subsumption) KEG seminar

  4. Classification of ontology mapping techniques KEG seminar

  5. Modelling of interdependencies (1) • Using Bayesian Networks • String distance metrics from SecondString library (mapping methods) • Training data, pairs of concepts from ontologies ekaw.owl a confOf.owl from OntoFarm collection • 798 pairs • Bayesian network • nodes: mapping justification by each mapping method • Classification node: „align“ (true, false) KEG seminar

  6. Modelling of interdependencies (2) • Two tested Bayesian Networks (two corresponding classifiers) • Naive Bayesian Structure • Probability distributions learned from data • Learned Bayesian Structure • Learned both CPT and structure KEG seminar

  7. Evaluation of models • One-leave-out method (798x) • Evaluation: precision, recall • Precision more important than recall • 3:2 (precision weight 0,6), 4:1 (0,8) • C = P*a + R*b, kde a, b jsou váhy • higher C, better classifier KEG seminar

  8. 73% precision, 60% recall, 88% accuracy at 80% threshold KEG seminar

  9. 84% precision, 53% recall, 89% accuracy at 60% threshold Align ci. CharJaccard, Monge-Elkan, Levenshtein | TFIDF, SmithWaterman, Jaccard, Jaro, SLIM KEG seminar

  10. Evaluation (c = P*a + R*b) BN 2 Naive bayes Jaccard KEG seminar

  11. Mapping patterns (1) • Capturing structures using mapping patterns • Mapping pattern between ontologies KEG seminar

  12. Mapping patterns (2) Mapping pattern Part of Bayesian Network KEG seminar

  13. Conclusions & Future works • Combination of string-based methods is not promising • Implementation of low-level „string based justifications“ of mapping – suffix, prefix, identical names • Capturing context – Employ methodsworking with structuresof ontologies (graph-based), mapping patterns • Not only equivalence relations, but also discovery subsumption relations –using linguistic sources, like WordNet KEG seminar

  14. Ontology Alignment Evaluation Initiative 2006 - 'Conference' Track KEG seminar

  15. OAEI 2006 at ISWC’06 • Evaluation initiative in Ontology matching • Since 2004 • In 2006 OAEI workshop at Ontology matching workshop, ISWC • Four tracks (six data sets) • Benchmark (biblio), • Expressive ontologies: anatomy (2 ontologies 10k classes), jobs (jobs and jobs seekers, real world case) • Directory (web sites directory) – 4 thousand elementary test, Food data set– SKOS thesaurus about food with other food ontologies KEG seminar

  16. Conference track • Coordinated by UEP • Free exploration by participants within 10 ontologies • Domain: conference organisation • No a priori reference alignment • Participants: 6 research groups KEG seminar

  17. Ontologies in track KEG seminar

  18. Participants (1) • Combination of methods: lexicographic and contextual • ISLab • 1:1 matching approach • Linguistic technique - thesaurus of terms and weighted terminological relationships is exploited • Contextual technique - semantic relation in an ontology • RiMOM • Ontology alignment defined as a directional one • Matchers: Name-based (also NLP methods), Instance-based, Description-based, Taxonomy context-based, Constraints-based • CtxMatch • DL formulas • Not only eq., also subsumption, disjointness, intersection KEG seminar

  19. Participants (2) • COMA++ • Extension of COMA • Automs • Lexical matching method, LSI, structural matching algorithm • Falcon • elementary matchers: string-based, graph-based KEG seminar

  20. Evaluation (1) • Personal judgement of organisers • interesting individual correspondences (inverse compound names, eg. PC_Member = Member_PC), synonyms • Mapping errors: subsumption, inversion role, siblings, lexical confusion • Mapping between relation and class, eg. has_an_email and E-mail KEG seminar

  21. Evaluation (2) KEG seminar

  22. Evaluation (3) • Subsumption error • Author,Paper_Author • Conference_Trip, Conference_part • Inversion role error • abstract_of_paper,reviewerOfPaper error • Siblings • ProgramCommittee,Technical_commitee • Lexical confusion error • program,Program_chair • Relation – Class mapping • has_enddate,Date • hasTitle,Title; hasSurname,Surname KEG seminar

  23. Evaluation (4) KEG seminar

  24. Summary • How to evaluate this track? • Interesting mappings • Recall? KEG seminar

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