A Probabilistic-Logical Framework for Ontology Matching

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

Positive Aspects: No data available now.

Negative Aspects: No data available now.

Limitations: No data available now.

Challenges: No data available now.

Proposes Algorithm: No data available now.

Methodology: No data available now.

Requirements: No data available now.

Limitations: No data available now.

Implementations

Download-page: No data available now.

Access API: No data available now.

Information Representation: No data available now.

Data Catalogue: {{{Catalogue}}}

Runs on OS: No data available now.

Vendor: No data available now.

Uses Framework: No data available now.

Has Documentation URL: No data available now.

Programming Language: No data available now.

Version: No data available now.

Platform: No data available now.

Toolbox: No data available now.

GUI: No

Research Problem

Subproblem of: No data available now.

RelatedProblem: No data available now.

Motivation: No data available now.

Evaluation

Experiment Setup: No data available now.

Evaluation Method : No data available now.

Hypothesis: No data available now.

Description: No data available now.

Dimensions: {{{Dimensions}}}

Benchmark used: No data available now.

Results: No data available now.