Combining Ontology Mapping Methods Using Bayesian Networks
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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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Ond ej v b vojt ch sv tek

Combining Ontology Mapping Methods Using Bayesian NetworksOntology Alignment Evaluation Initiative 2006 - 'Conference' Track

Ondřej Šváb

Vojtěch Svátek

KEG seminar


Overview

Overview

  • Ontology Mapping

  • Combining Ontology Mapping Methods

  • Using Bayesian Networks

    • String distance metrics

    • Mapping patterns

  • OAEI

    • Our track – conference domain

    • Evaluation

KEG seminar


Ond ej v b vojt ch sv tek

Ontology Mapping

Ontology Mapping = discovering of Semantic correspondencies (equivalence, subsumption)

KEG seminar


Classification of ontology mapping techniques

Classification of ontology mapping techniques

KEG seminar


Model ling of interdependencies 1

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


Model ling of interdependencies 2

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


Evaluation of models

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


Ond ej v b vojt ch sv tek

73% precision, 60% recall, 88% accuracy

at 80% threshold

KEG seminar


Ond ej v b vojt ch sv tek

84% precision, 53% recall, 89% accuracy

at 60% threshold

Align ci. CharJaccard, Monge-Elkan, Levenshtein | TFIDF,

SmithWaterman, Jaccard, Jaro, SLIM

KEG seminar


Evaluation c p a r b

Evaluation (c = P*a + R*b)

BN 2

Naive bayes

Jaccard

KEG seminar


Mapping patterns 1

Mapping patterns (1)

  • Capturing structures using mapping patterns

  • Mapping pattern between ontologies

KEG seminar


Mapping patterns 2

Mapping patterns (2)

Mapping pattern

Part of Bayesian Network

KEG seminar


Conclusions future works

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


Ontology alignment evaluation initiative 2006 conference track

Ontology Alignment Evaluation Initiative 2006 - 'Conference' Track

KEG seminar


Oaei 2006 at iswc 06

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


Conference track

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


Ontologies in track

Ontologies in track

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Participants 1

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


Participants 2

Participants (2)

  • COMA++

    • Extension of COMA

  • Automs

    • Lexical matching method, LSI, structural matching algorithm

  • Falcon

    • elementary matchers: string-based, graph-based

KEG seminar


Evaluation 1

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


Evaluation 2

Evaluation (2)

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Evaluation 3

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


Evaluation 4

Evaluation (4)

KEG seminar


Summary

Summary

  • How to evaluate this track?

    • Interesting mappings

  • Recall?

KEG seminar


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