Semantic integration: a survey of ontology-based approaches
ACM SIGMOD Record
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
ACM SIGMOD Record
Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics
OpenMath Content Dictionaries for SI Quantities and Units
Calculemus '09/MKM '09 Proceedings of the 16th Symposium, 8th International Conference. Held as Part of CICM '09 on Intelligent Computer Mathematics
Semantic precision and recall for ontology alignment evaluation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Ontology matching with semantic verification
Web Semantics: Science, Services and Agents on the World Wide Web
AgreementMaker: efficient matching for large real-world schemas and ontologies
Proceedings of the VLDB Endowment
Ontology alignment evaluation initiative: six years of experience
Journal on data semantics XV
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Ontologies provide a formal description of concepts and their relationships in a knowledge domain. The goal of ontology alignment is to identify semantically matching concepts and relationships across independently developed ontologies that purport to describe the same knowledge. In order to handle the widest possible class of ontologies, many alignment algorithms rely on terminological and structural methods, but the often fuzzy nature of concepts complicates the matching process. However, one area that should provide clear matching solutions due to its mathematical nature, is units of measurement. Several ontologies for units of measurement are available, but there has been no attempt to align them, notwithstanding the obvious importance for technical interoperability. We propose a general strategy to map these (and similar) ontologies by introducing MathML to accurately capture the semantic description of concepts specified therein. We provide mapping results for three ontologies, and show that our approach improves on lexical comparisons.