Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
XClust: clustering XML schemas for effective integration
Proceedings of the eleventh international conference on Information and knowledge management
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
The Chimaera Ontology Environment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Interactive deduplication using active learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Active learning with multiple views
Active learning with multiple views
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Ontology Matching
Using Bayesian decision for ontology mapping
Web Semantics: Science, Services and Agents on the World Wide Web
Using Google distance to weight approximate ontology matches
Proceedings of the 16th international conference on World Wide Web
Leveraging data and structure in ontology integration
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Analyzing and revising data integration schemas to improve their matchability
Proceedings of the VLDB Endowment
Learning Concept Mappings from Instance Similarity
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
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
Selective supervision: guiding supervised learning with decision-theoretic active learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Bootstrapping ontology alignment methods with APFEL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Learning chinese entity attributes from online encyclopedia
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
RDFKB: a semantic web knowledge base
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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
Combining human and computation intelligence: the case of data interlinking tools
International Journal of Metadata, Semantics and Ontologies
CrowdMap: crowdsourcing ontology alignment with microtasks
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Combining human and computation intelligence: the case of data interlinking tools
International Journal of Metadata, Semantics and Ontologies
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Ontology matching plays a key role for semantic interoperability. Many methods have been proposed for automatically finding the alignment between heterogeneous ontologies. However, in many real-world applications, finding the alignment in a completely automatic way is highly infeasible . Ideally, an ontology matching system would have an interactive interface to allow users to provide feedbacks to guide the automatic algorithm. Fundamentally, we need answer the following questions: How can a system perform an efficiently interactive process with the user? How many interactions are sufficient for finding a more accurate matching? To address these questions, we propose an active learning framework for ontology matching, which tries to find the most informative candidate matches to query the user. The user's feedbacks are used to: 1) correct the mistake matching and 2) propagate the supervise information to help the entire matching process. Three measures are proposed to estimate the confidence of each matching candidate. A correct propagation algorithm is further proposed to maximize the spread of the user's "guidance". Experimental results on several public data sets show that the proposed approach can significantly improve the matching accuracy (+8.0% better than the baseline methods).