CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Knocking the door to the deep Web: integrating Web query interfaces
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Discovering complex matchings across web query interfaces: a correlation mining approach
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Editorial: special issue on web content mining
ACM SIGKDD Explorations Newsletter
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Towards Building a MetaQuerier: Extracting and Matching Web Query Interfaces
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Why is schema matching tough and what can we do about it?
ACM SIGMOD Record
A holistic paradigm for large scale schema matching
A holistic paradigm for large scale schema matching
A Two-Stage Clustering Algorithm for Multi-type Relational Data
SNPD '08 Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
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The current needs of information integration of Deep Web sources, schema matching is one of the most important ways of data integration, E-business, data warehousing, and semantic query processing. However in today systems, schema matching has some significant limitations. There are some problems in schema matching which often have some hidden regularities and semantic conflicts over many sources. These can be essentially leveraged in enabling semantics discover of schema matching. In this paper, we focus on the specific problem of semantic heterogeneity between relational databases schema matching. We propose a clustering algorithm with metadata-labeling from our system's ontology. And also this paper represents a same representation model for all relational schemas with their own ontologies to map the best results of semantic mapping. In this paper, we compare the experimental results of schema matching for relational databases.