Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Towards general measures of comparison of objects
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
The three semantics of fuzzy sets
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Learning object identification rules for information integration
Information Systems - Data extraction, cleaning and reconciliation
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fuzzy Annotation of Web Data Tables Driven by a Domain Ontology
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Combining a Logical and a Numerical Method for Data Reconciliation
Journal on Data Semantics XII
L2R: a logical method for reference reconciliation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Flexible SPARQL Querying of Web Data Tables Driven by an Ontology
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Google fusion tables: data management, integration and collaboration in the cloud
Proceedings of the 1st ACM symposium on Cloud computing
Google fusion tables: web-centered data management and collaboration
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers
IEEE Transactions on Fuzzy Systems
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We present, in this paper, a duplicate detection method in semantically annotated Web data tables, driven by a domain Termino-Ontological Resource (TOR). Our method relies on the fuzzy semantic annotations automatically associated with the Web data tables. A fuzzy semantic annotation is automatically associated with each row of a Web data table. It corresponds to the instantiation of a composed concept of the domain TOR, which represents the semantic n-ary relationship that exists between the columns of the Web data table. A fuzzy semantic annotation contains fuzzy values expressed as fuzzy sets. We propose an automatic duplicate detection method which consists in detecting the pairs of duplicate fuzzy semantic annotations and relies on (i) knowledge declared in the domain TOR and on (ii) similarity measures between fuzzy sets. Two new similarity measures are defined to compare both, the symbolic fuzzy values and the numerical fuzzy values. Our method has been tested on a real application in the domain of chemical risk in food.