Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Ontology Matching
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Semantic matching: algorithms and implementation
Journal on data semantics IX
Matching hierarchical classifications with attributes
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Debugging and semantic clarification by pinpointing
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Learning Disjointness for Debugging Mappings between Lightweight Ontologies
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
A Kernel Revision Operator for Terminologies -- Algorithms and Evaluation
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
The Relevance of Reasoning and Alignment Incoherence in Ontology Matching
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
An Efficient Method for Computing Alignment Diagnoses
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
A Conflict-Based Operator for Mapping Revision
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Candidate reduction and alignment improvement techniques used in aligning ontologies
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
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Automatically discovering semantic relations between ontologies is an important task with respect to overcoming semantic heterogeneity on the semantic web. Ontology matching systems, however, often produce erroneous mappings. In this paper we propose a method for optimizing precision and recall of existing matching systems. The principle of this method is based on the idea that it is possible to infer logical constraints by comparing subsumption relations between concepts of the ontologies to be matched. In order to verify this principle we implemented a system that uses our method as basis for optimizing mappings. We generated a set of synthetic ontologies and corresponding defective mappings and studied the behavior of our method with respect to the properties of the matching problem. The results show that our strategy actually improves the quality of the generated mappings.