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Inexact Matching of Ontology Graphs Using Expectation-Maximization
Prashant Doshi1, Ravikanth Kolli, Christopher Thomas
1LSDIS Lab, Dept. of Computer Science, University of Georgia, Athens, GA 30602, PDOSHI@CS.UGA.EDU.
Web Semantics (Online)
|February 18, 2010
Summary
We developed a novel method for ontology matching using expectation-maximization (EM) to map similar domain schemas. This approach handles inexact matching and scales to large ontologies for improved data integration.
Area of Science:
- Computer Science
- Artificial Intelligence
- Data Management
Background:
- Decentralized development and publication of ontological data necessitate effective ontology matching.
- Existing methods for ontology matching face challenges with scalability and handling inexact matches.
Purpose of the Study:
- To present a new method for mapping ontology schemas that address similar domains.
- To address the crucial problem of ontology matching in a decentralized data landscape.
Main Methods:
- Formulated ontology matching as a maximum likelihood problem, solved using expectation-maximization (EM).
- Modeled ontology schemas as directed graphs and employed a generalized EM for node mapping.
- Integrated structural, lexical, and instance similarity for potentially inexact matching.
- Adapted generalized EM with a memory-bounded partitioning scheme to handle large ontologies.
Main Results:
- The proposed method effectively performs ontology mapping, including inexact matching scenarios.
- Experimental results on established benchmarks demonstrate the method's efficacy and scalability.
- Identified and addressed computational bottlenecks for large-scale ontology alignment.
Conclusions:
- The developed expectation-maximization based method offers a robust solution for ontology schema mapping.
- The approach successfully integrates diverse similarity measures and handles computational challenges.
- This work contributes to more effective data integration in decentralized semantic web environments.
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