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Communications of the ACM
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Ontology mapping: the state of the art
The Knowledge Engineering Review
Semantic integration: a survey of ontology-based approaches
ACM SIGMOD Record
ACM SIGMOD Record
Ontology Matching
A corpus-driven approach for design, evolution and alignment of ontologies
Proceedings of the 38th conference on Winter simulation
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
Applying Logical Constraints to Ontology Matching
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
A gauss function based approach for unbalanced ontology matching
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Improving Ontology Matching Using Meta-level Learning
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Neural network based constraint satisfaction in ontology mapping
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Towards automatic merging of domain ontologies: The HCONE-merge approach
Web Semantics: Science, Services and Agents on the World Wide Web
CSR: discovering subsumption relations for the alignment of ontologies
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Bootstrapping ontology alignment methods with APFEL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Supervised learning of an ontology alignment process
WM'05 Proceedings of the Third Biennial conference on Professional Knowledge Management
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Ontology alignment is required to enable interoperability between applications using different ontologies. The alignments generated allow knowledge to be shared between semantically equivalent concepts in two different ontologies. Some applications carry out the ontology alignment process offline and the accuracy of the alignments generated is much more important than the performance of the process. In other applications that perform the ontology alignment process online, the performance is as important as the accuracy of the alignments generated because the process needs to be fast. In this paper, we present techniques used to reduce the number of candidates for matching and techniques used to improve the accuracy of alignments in the ontology alignment process. Candidate reduction methods compare entities in the source ontology to a subset of entities in the target ontology by filtering entities which are unlikely to be matched instead of extensively comparing all pairs of entities belonging to two different ontologies. These techniques reduce the time taken to align the ontologies and are categorised depending on whether they perform candidate reduction using structural methods, external resources or iterative methods. On the other hand, techniques to improve the accuracy of alignments output a set of alignments with greater accuracy given an initial set of alignments. Some of these techniques are constraint-based while others are not.