Difference between revisions of "A Probabilistic-Logical Framework for Ontology Matching"

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|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  
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|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.
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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
 
|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.
 
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.

Revision as of 21:21, 2 July 2018

A Probabilistic-Logical Framework for Ontology Matching
A Probabilistic-Logical Framework for Ontology Matching
Bibliographical Metadata
Subject: Ontology Matching
Year: 2010
Authors: Mathias Niepert, Christian Meilicke, Heiner Stuckenschmidt
Venue AAAI
Content Metadata
Problem: No data available now.
Approach: No data available now.
Implementation: No data available now.
Evaluation: No data available now.

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.

Approach

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Implementations

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Evaluation

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Facts about "A Probabilistic-Logical Framework for Ontology Matching"
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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 approachNo data available now. +
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. +
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Has subjectOntology Matching +
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TitleA Probabilistic-Logical Framework for Ontology Matching +
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