Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Semantic integration: a survey of ontology-based approaches
ACM SIGMOD Record
Ontology Matching
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
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
IEEE Transactions on Knowledge and Data Engineering
Markov network based ontology matching
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Bootstrapping ontology alignment methods with APFEL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Using pseudo feedback to improve cross-lingual ontology mapping
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
A clustering-based approach to ontology alignment
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Heterogeneous web data search using relevance-based on the fly data integration
Proceedings of the 21st international conference on World Wide Web
Automatic configuration selection using ontology matching task profiling
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Proceedings of the 3rd Annual ACM Web Science Conference
A configurable translation-based cross-lingual ontology mapping system to adjust mapping outcomes
Web Semantics: Science, Services and Agents on the World Wide Web
A unified approach to matching semantic data on the Web
Knowledge-Based Systems
Instance-Based matching of large ontologies using locality-sensitive hashing
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
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A key problem in ontology alignment is that different ontological features (e.g., lexical, structural or semantic) vary widely in their importance for different ontology comparisons. In this paper, we present a set of principled techniques that exploit user feedback to customize the alignment process for a given pair of ontologies. Specifically, we propose an iterative supervised-learning approach to (i) determine the weights assigned to each alignment strategy and use these weights to combine them for matching ontology entities; and (ii) determine the degree to which the information from such matches should be propagated to their neighbors along different relationships for collective matching. We demonstrate the utility of these techniques with standard benchmark datasets and large, real-world ontologies, showing improvements in F-scores of up to 70% from the weighting mechanism and up to 40% from collective matching, compared to an unweighted linear combination of matching strategies without information propagation.