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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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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

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Ontology Mapping

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

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

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73% precision, 60% recall, 88% accuracy

at 80% threshold

KEG seminar

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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

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

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)

KEG seminar

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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