Federated database systems for managing distributed, heterogeneous, and autonomous databases
ACM Computing Surveys (CSUR) - Special issue on heterogeneous databases
WordNet: a lexical database for English
Communications of the ACM
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
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
Semantic-integration research in the database community
AI Magazine - Special issue on semantic integration
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Supporting ontology-based semantic matching in RDBMS
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Quickmig: automatic schema matching for data migration projects
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Concept Similarity Matching Based on Semantic Distance
SKG '08 Proceedings of the 2008 Fourth International Conference on Semantics, Knowledge and Grid
AgreementMaker: efficient matching for large real-world schemas and ontologies
Proceedings of the VLDB Endowment
IMB3-Miner: mining induced/embedded subtrees by constraining the level of embedding
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A survey of schema-based matching approaches
Journal on Data Semantics IV
Enforcing strictness in integration of dimensions: beyond instance matching
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP
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Concept matching is important when heterogeneous data sources are to be merged for the purpose of knowledge sharing. It has many useful applications in areas such as schema matching, ontology matching, scientific knowledge management, e-commerce, enterprise application integration, etc. With the desire of knowledge sharing and reuse in these fields, merging commonly occurs among different organizations where the knowledge describing the same domain is to be matched. Due to the different naming conventions, granularity and the use of concepts in different contexts, a semantic approach to this problem is preferred in comparison to syntactic approach that performs matches based upon the labels only. We propose a concept matching method that initially does not consider labels when forming candidate matches, but rather utilizes structural information to take the context into account and detect complex matches. Real world knowledge representations (schemas) are used to evaluate the method.