Semantic integration of semistructured and structured data sources
ACM SIGMOD Record
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
IEEE Intelligent Systems
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
Ontology mapping: the state of the art
The Knowledge Engineering Review
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Semantic integration: a survey of ontology-based approaches
ACM SIGMOD Record
ACM SIGKDD Explorations Newsletter
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A method to combine linguistic ontology-mapping techniques
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Debugging and semantic clarification by pinpointing
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
A survey of schema-based matching approaches
Journal on Data Semantics IV
Matching unstructured vocabularies using a background ontology
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
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We combine the Semantic Web with the Web, as background knowledge, to provide a more balanced solution for Ontology Mapping. The Semantic Web can provide mappings that are missed by the Web, which can provide many more, but noisy, mappings. We present a combined technique that is based on variations of existing approaches. Our experimental results in two real-life thesauri are compared with previous work, and they reveal that a combined approach to Ontology Mapping can provide more balanced results in terms of precision, recall and confidence measure of mappings. We also discover that a reduced set of 3 appropriate Hearst patterns can eliminate noise in the list of discovered mappings, and thus techniques based exclusively in the Web can be improved. Finally, we also identify open questions derived from building a combined approach.