Ontology Mapping

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Ontology Mapping. I3CON Workshop PerMIS August 24-26, 2004 Washington D.C., USA Marc Ehrig Institute AIFB, University of Karlsruhe. Agenda. Motivation Definitions Mapping Process Efficiency Evaluation Conclusion. Motivation. Semantic Web Many individual ontologies - PowerPoint PPT Presentation

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  • Ontology MappingI3CON WorkshopPerMISAugust 24-26, 2004Washington D.C., USAMarc EhrigInstitute AIFB, University of Karlsruhe

    Ontology Mapping

  • AgendaMotivationDefinitionsMapping ProcessEfficiencyEvaluationConclusion

    Ontology Mapping

  • MotivationSemantic WebMany individual ontologiesDistributed collaborationInteroperability requiredAutomatic effective mapping necessary

    Ontology Mapping

  • Mapping DefinitionGiven two ontologies O1 and O2, mapping one ontology onto another means that for each entity (concept C, relation R, or instance I) in ontology O1, we try to find a corresponding entity, which has the same intended meaning, in ontology O2.map(e1i) = e2j

    Complex mappings are not addressed: n:m, concept-relation,

    Ontology Mapping

  • AgendaMotivationDefinitionsMapping ProcessEfficiencyEvaluationConclusion

    Ontology Mapping

  • ProcessOutputFeaturesSimilarityAggregationInterpretationEntity PairSelection

    Ontology Mapping

  • Features

    ObjectVehicleCarBoathasOwnerOwnerSpeedhasSpeedPorsche KA-123Marc250 km/h

    Ontology Mapping

  • Similarity MeasureString similarity

    Object Similarity

    Set similarity

    Ontology Mapping

  • Similarity Rules

    Ontology Mapping

  • ProcessOutputFeaturesSimilarityAggregationInterpretationEntity PairSelection

    Ontology Mapping

  • CombinationHow are the individual similarity measures combined?

    LinearlyWeightedSpecial Function

    Ontology Mapping

  • InterpretationFrom similarities to mappings

    Thresholdmap(e1j) = e2j sim(e1j ,e2j)>t

    Ontology Mapping

  • Example

    Ontology Mapping

  • AgendaMotivationDefinitionsMapping ProcessEfficiencyEvaluationConclusion

    Ontology Mapping

  • Critical OperationsComplete comparison of all entity pairsExpensive features e.g. fetching of all (inferred) instances of a conceptCostly heuristics e.g. Syntactic Similarity

    Ontology Mapping

  • AssumptionsComplete comparison unnecessary.Complex and costly methods can in essence be replaced by simpler methods.

    Ontology Mapping

  • Reduction of ComparisonsRandom SelectionClosest LabelChange PropagationCombination

    Ontology Mapping

  • Removal of Complex Featuresall subclassOfdirect subclassOfall instancesdirect instances

    Ontology Mapping

  • Complexityc = (feat + sel + comp (k simk + agg) + inter) iter

    NOMc = O((n + n2 + n2 (log2(n) + 1) + n) 1)= O(n2 log2(n))PROMPT c = O((n + n2 + n2 (1 + 0) + n) 1)= O(n2)QOM c = O((n + nlog(n) + n (1 + 1) + n) 1)= O(n log(n))

    Ontology Mapping

  • AgendaMotivationDefinitionsMapping ProcessEfficiencyEvaluationConclusion

    Ontology Mapping

  • ScenariosTravel domain: Russia500 entitiesManual assigned mappings by test group

    Ontology Mapping

  • Precision

    Ontology Mapping

  • Recall

    Ontology Mapping

  • F-measure

    Ontology Mapping

  • Efficiency

    Ontology Mapping

  • AgendaMotivationDefinitionsMapping ProcessEfficiencyEvaluationConclusion

    Ontology Mapping

  • ConclusionAutomatic mappings are necessary.Semantics help to determine better mappings.Efficient approaches needed as ontology numbers and size increase.Complexity of measures can be reduced.Number of mapping candidates can be reduced.Loss of quality is marginal.Good increase in efficiency.

    Ontology Mapping

  • OutlookMachine learning to adapt to dynamically changing ontology environmentsIncrease evaluation basisAddition of background knowledge e.g. WordNetIntegration into ontology applications e.g. for merging

    Ontology Mapping

  • Thank you.

    Ontology Mapping

    WelcomeIdea of having one world ontology is dismissed.One approach is to merge ontologies already at creation.The other is to map them when required.Rather decentralized, uncoordinated, individual ontologies.Big number of definitions around mapping, merging, alignment, integration, mediation, etc..Our definition given here.Not complex mappings such as, concept-relation or n-ary mappings.Example ontologyDescribe the features: labels, taxonomy (super- and subclass), instances, relations, domain, range, mother concept, instantiated relationsFeatures can also be domain dependent: hashCode of file, mimeType

    Betonung auch auf andere Eigenschaften als Label

    String comparisons e.g. labels.Direct object comparisons; are two objects the same? E.g. domain. Set comparisons; are the two sets of objects the same? E.g. instances.The latter require a precalculated similarity of the objects based on previous rounds.For each ontology feature there is an adequate similarity measureFrom each rule we receive a similarity valueList not complete and not only way to do it e.g. labels could also be compared using a background dictionary (WordNet)

    For the holistic approach the combination of the rules is essential.Threshold determined through test data sets.Two ontologies.Mapping between Car and Automobile?Existing mappings from before.Individual rules.Result.N is size of ontologies; worst case Considerations use average ontology tree, not string

    Feature selection: transformation of ontology format, selection of features O(n)Pair selection: labels O(n*log(n)), change propagation O(n)Pair comparisons: number of pairs limited O(n)Individual similarity: lowered, constant O(1)Aggregation: constant number of individual similaritiesInterpretation: constantIteration: constant; during the first iteration the mappings are found through labels, these are evenly distributed; the number of steps to reach every possible mapping pair constant; number of iterations also constantStatement retrieval in ontology DB is assumed to be linearOntologies created by students.Correct mappings assigned manually.Mappings arranged by confidence similarity value.Precision: correct mappings of all found mappings.First mappings were correct for both strategies.Circle indicates where automatic threshold would cut-off. Results of the sigmoid function generally higher.Correct found mappings of all possible mappings.Found correct mappings considerably higher than label strategy.Especially, when considering circle.F-measure combines both precision and recall.Conclusion and Results

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