View source for A Probabilistic-Logical Framework for Ontology Matching

{{Paper
|Title=A Probabilistic-Logical Framework for Ontology Matching
|Subject=Ontology Matching
|Authors=Mathias Niepert, Christian Meilicke, Heiner Stuckenschmidt,
|Series=AAAI
|Year=2010
|Abstract=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.
|Conclusion=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.
|Future work=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.
|Problem=Link Discovery
|Approach=Probabilistic-logical framework for ontology matching based on Markov logiP
|Implementation=ml-match
|Evaluation=Using thresholds on the a-priori similarity measure
|PositiveAspects=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.
|NegativeAspects=No data available now.
|Limitations=No data available now.
|Challenges=No data available now.
|ProposesAlgorithm=No data available now.
|Model=Mathematical
|Methodology=No data available now.
|Requirements=training data
|Download-page=http://code.google.com/p/ml-match/
|API=No data available now.
|InfoRepresentation=No data available now.
|OS=No data available now.
|vendor=No data available now.
|Framework=TheBeast
|DocumentationURL=https://code.google.com/archive/p/ml-match/wikis/MLExample.wiki
|ProgLang=No data available now.
|Version=No data available now.
|Platform=No data available now.
|Toolbox=No data available now.
|GUI=No
|Subproblem=Ontology Alignment
|RelatedProblem=Concept similarity
|Motivation=matching ontologies enables the knowledge and data expressed in the matched ontologies to interoperate.
|ExperimentSetup=All experiments were conducted on a desktop PC with AMD Athlon Dual Core Processor 5400B with 2.6GHz and 1GB RAM.
|EvaluationMethod=Average 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.
|Hypothesis=No data available now.
|Description=We 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.
|Dimensions=Accuracy
|Benchmark=Ontofarm dataset
|Results=Using stability constraints improves alignment quality with both learned and manually set weights.
}}