a review of ontology mapping, merging, and integration

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A Review of Ontology Mapping, Merging, and Integration. Presenter: Yihong Ding. Survey Papers. Ontology Research and Development Part 2 – A review of Ontology Mapping and Evolving, Ying Ding and Schubert Foo - PowerPoint PPT Presentation


  • A Review of Ontology Mapping, Merging, and IntegrationPresenter: Yihong Ding

  • Survey PapersOntology Research and Development Part 2 A review of Ontology Mapping and Evolving, Ying Ding and Schubert Foo

    Some Issues on Ontology Integration, H. Sofia Pinto, A. Gomez-Perez, and Joao P. Martins

  • Ontology MappingTwo parties understand each otherUse the same formal representation Share the conceptualization (so the same ontology)

    Not easy to let everybody to agree on the same ontology for a domain

    The problem of ontology mappingDifferent ontologies on the same domainParties with different ontologies do not understand each other

  • Ontology IntegrationBuilding a new ontology and reusing other available ontologies (integration)

    Merging different ontologies into a single one that unifies all of them (merging)

    Integration of ontologies into applications (use)

  • IntegrationResulting ontology can be composed of several modules

    Be able to identify regions taken from different integrated ontologies

  • MergingHard to identify regions taken from merged ontologies

    Knowledge from merged ontologies is homogenized

    Knowledge from one source ontology is scattered and mingled with the knowledge that comes from other sources

  • UseOntologies should be compatible among themselves

    Issues for compatibilityOntological commitmentsLanguageLevel of detailsContextetc.

  • InfoSleuths reference ontologyMappingExplicit specified relationships of terms between ontologiesEncapsulated within resource agentsResource agentEncapsulate information about mapping rulesPresent information in ontologies (reference ontologies)Reference ontologiesRepresented in OKBCStored in OKBC serverOntology agents provide specifications To users (for request formulation)To resource agents (for mapping)

  • Stanfords ontology algebraMappingEstablished articulations that enables the knowledge interoperabilityExecuted by ontology algebraOntology algebraOperatorsUnary: filter, extractBinary: intersection, union, differenceInputs: ontology graphsSemi-automatic graph mappingDomain experts define a variety of fuzzy matchingUse articulation ontology (abstract mathematical entities with some properties)

  • AIFBs formal concept analysisMapping and mergingOntology concepts with the same extensionExecuted by FCA-MergeFCA-MergeCreate a concept hierarchy - the concept lattice -containing the original concepts based on the source ontologies ProcessObjects annotated by both ontologies: directly compute latticeElse: create annotated objects first.Else if cannot annotate: use documents as artificial objects. I.e., concepts which always appear in the same documents are supposed to be merged

  • ECAI2000s methodsWilliams & TsatsoulisSupervised inductive learningCreate semantic concept descriptionsApply concept clustering algorithm to find mapping

    Tamma & Bench-CaponName-based matchingRelate classes in bottom-up and top-down waysPriority functions to solve inconsistencyHuman experts adjust priority functions

    UscholdUse a global reference ontology

  • ISIs OntoMorphSyntactic rewritingPattern-directed rewrite rulesConcise specification of sentence-level transformations based on pattern matching

    Semantic rewritingModulate syntactic rewriting via semantic models and logical inference

  • KRAFTs ontology clusteringBased on the similarities between the concepts known to different agentsMethodUse a domain ontology describe abstract information (global reference)Each ontology cluster define certain part of its parent ontologyName, instance, relation, compound matchers

  • Heterogeneous Database IntegrationA database scheme is a lightweight ontologyTypical researchesBatini et.al. (1986), five steps of integrating schemata of existing or proposed databases into a global, unified schemaSheth & Kashyap (1992), semantic similarities in schema integrationPalopoli et.al. (2000), two techniques to integrate and abstract database schemes

  • Other Ontology MappingsLehmann & Cohn (1994)Need more specialized concept definitions

    Li (1995)Identify attribute similarities using neural networks

    Borst & Akkermans (1997)Resulted mappings could be considered as a new ontology

  • Other Ontology MappingsHovy (1998)Several heuristic rules to support the merging of ontologies

    Weinstein & Birmingham (1999)Graph mapping use description compatibility between elements

    McGuinness et.al. (2000)Chimaera systemTerm merging from different knowledge sources

    Noy & Musen (2000)PROMPT algorithm for Protg systemOntology merging and alignment for OKBC compatible format

  • ConclusionDepend very much on the inputs of human expertsFocus on 1-1 mappingsFurther needs n:1, 1:n, m:n mappings Ontology mapping can be viewed as the projection of the general ontologies from different point of views


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