Visualization of mappings between schemas
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Queue - Semi-structured Data
Ontology Matching
Ontology visualization methods—a survey
ACM Computing Surveys (CSUR)
Ten Challenges for Ontology Matching
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems
Comparing human and automatic thesaurus mapping approaches in the agricultural domain
DCMI '08 Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications
Discovering Missing Background Knowledge in Ontology Matching
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Faceted Lightweight Ontologies
Conceptual Modeling: Foundations and Applications
Reasoning Support for Mapping Revision
Journal of Logic and Computation
A cognitive support framework for ontology mapping
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Semantic matching: algorithms and implementation
Journal on data semantics IX
A large scale taxonomy mapping evaluation
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Web explanations for semantic heterogeneity discovery
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Ontology alignment evaluation initiative: six years of experience
Journal on data semantics XV
Domains and context: First steps towards managing diversity in knowledge
Web Semantics: Science, Services and Agents on the World Wide Web
A semantic geo-catalogue for a local administration
Artificial Intelligence Review
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Identifying semantic correspondences between different vocabularies has been recognized as a fundamental step towards achieving interoperability. Several manual and automatic techniques have been recently proposed. Fully manual approaches are very precise, but extremely costly. Conversely, automatic approaches tend to fail when domain specific background knowledge is needed. Consequently, they typically require a manual validation step. Yet, when the number of computed correspondences is very large, the validation phase can be very expensive. In order to reduce the problems above, we propose to compute the minimal set of correspondences, that we call the minimal mapping, which are sufficient to compute all the other ones. We show that by concentrating on such correspondences we can save up to 99% of the manual checks required for validation.