Lazy Learning of Bayesian Rules
Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Comparison of Lazy Bayesian Rule and Tree-Augmented Bayesian Learning
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Leveraging data and structure in ontology integration
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
Survey of Improving Naive Bayes for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Swoop: A Web Ontology Editing Browser
Web Semantics: Science, Services and Agents on the World Wide Web
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In this paper, we describe a machine learning approach to ontology mapping. Although Machine learning techniques have been used earlier in many semantic integration approaches, dependence on precision recall curves to preset the weights and thresholds of the learning systems has been a serious bottleneck. By recasting the mapping problem to a classification problem we try to automate this step and develop a robust and extendable meta learning algorithm. The implication is that we can now extend the same method to map the ontology pairs with different similarity measures which might not be specialized for the specific domain, yet obtain results comparable to the state of the art mapping algorithms that exploit machine learning methods. Interestingly we see that as the similarity measures are diluted, our approach performs significantly better for unbalanced classes. We have tested our approach using several similarity measures and two real world ontologies, and the test results we discuss validate our claim. We also present a discussion on the benefits of the proposed meta learning algorithm.