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A Probabilistic-Logical Framework for Ontology Matching

Abstract[edit]

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Retrieved from "https://www.openresearch.org/wiki/A_Probabilistic-Logical_Framework_for_Ontology_Matching"
Facts about "A Probabilistic-Logical Framework for Ontology Matching"
RDF feed
Access APINo data available now. +
Event in seriesAAAI +
Has BenchmarkOntofarm dataset +
Has ChallengesNo data available now. +
Has DataCatalouge{{{Catalogue}}} +
Has DescriptionWe applied the reasoner Pellet to create the ground MLN formulation and used TheBeast2 (Riedel 2008) to convert the MLN formulations to the corresponding ILP instances. Finally, we applied the mixed integer programming solver SCIP3 to solve the ILP. +
Has DimensionsAccuracy +
Has DocumentationURLhttps://code.google.com/archive/p/ml-match/wikis/MLExample.wiki +
Has Downloadpagehttp://code.google.com/p/ml-match/ +
Has EvaluationUsing thresholds on the a-priori similarity measure +
Has EvaluationMethodAverage F1-values over the 21 OAEI reference alignments for manual weights vs. learned weights vs. formulation without stability constraints; thresholds range from 0.6 to 0.95. +
Has ExperimentSetupAll experiments were conducted on a desktop PC with AMD Athlon Dual Core Processor 5400B with 2.6GHz and 1GB RAM. +
Has GUINo +
Has HypothesisNo data available now. +
Has ImplementationMl-match +
Has InfoRepresentationNo data available now. +
Has LimitationsNo data available now. +
Has NegativeAspectsNo data available now. +
Has PositiveAspectsThe approach has several advantages over e …
The approach has several advantages over

existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of a-priori confidence values. In cases where training data is not available, weights set manually by experts still result in improved alignment quality. The framework is not only useful for aligning concepts and properties but can also include instance matching. For this purpose, one would only need to add a hidden predicate modeling instance correspondences. The resulting matching approach would immediately benefit from probabilistic joint inference, taking into account the interdependencies

between terminological and instance correspondences.
rminological and instance correspondences. +
Has Requirementstraining data +
Has ResultsUsing stability constraints improves alignment quality with both learned and manually set weights. +
Has SubproblemOntology Alignment +
Has VersionNo data available now. +
Has abstractOntology matching is the problem of determ …
Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a novel probabilistic-logical framework for ontology matching based on Markov logic. We define the syntax and semantics and provide a formalization of the ontology matching problem within the framework. The approach has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of apriori confidence values. We show empirically that the approach is efficient and more accurate than existing matchers on an established ontology alignment benchmark dataset.
shed ontology alignment benchmark dataset. +
Has approachProbabilistic-logical framework for ontology matching based on Markov logiP +
Has authorsMathias Niepert +, Christian Meilicke + and Heiner Stuckenschmidt +
Has conclusionWe presented a Markov logic based framewor …
We presented a Markov logic based framework for ontology matching capturing a wide range of matching strategies. Since these strategies are expressed with a unified syntax and semantics we can isolate variations and empirically evaluate their effects. Even though we focused only on a small subset of possible alignment strategies the results are already quite promising. We have also successfully learned weights for soft formulae within the framework. In cases where training data is not available, weights set manually by experts still result in improved alignment quality. Research related to determining appropriate weights based on structural properties of ontologies is a topic of future work.
s of ontologies is a topic of future work. +
Has future workThe framework is not only useful for align …
The framework is not only useful for aligning concepts and properties but can also include instance matching. For this purpose, one would only need to add a hidden predicate modeling instance correspondences. The resulting matching approach would immediately benefit from probabilistic joint inference, taking into account the interdependencies between terminological and instance correspondences.
rminological and instance correspondences. +
Has motivationmatching ontologies enables the knowledge and data expressed in the matched ontologies to interoperate. +
Has platformNo data available now. +
Has problemLink Discovery +
Has relatedProblemConcept similarity +
Has subjectOntology Matching +
Has vendorNo data available now. +
Has year2010 +
ImplementedIn ProgLangNo data available now. +
Proposes AlgorithmNo data available now. +
RunsOn OSNo data available now. +
TitleA Probabilistic-Logical Framework for Ontology Matching +
Uses FrameworkTheBeast +
Uses MethodologyNo data available now. +
Uses ToolboxNo data available now. +