Efficient algorithms for finding maximum matching in graphs
ACM Computing Surveys (CSUR)
A Theory of Attributed Equivalence in Databases with Application to Schema Integration
IEEE Transactions on Software Engineering
Explaining ambiguity in a formal query language
ACM Transactions on Database Systems (TODS)
Semantic vs. structural resemblance of classes
ACM SIGMOD Record
System-Guided View Integration for Object-Oriented Databases
IEEE Transactions on Knowledge and Data Engineering
View Integration: A Step Forward in Solving Structural Conflicts
IEEE Transactions on Knowledge and Data Engineering
A Graph-Based Approach For Extracting Terminological Properties of Elements of XML Documents
Proceedings of the 17th International Conference on Data Engineering
Using Schema Matching to Simplify Heterogeneous Data Translation
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Semantic Access: Semantic Interface for Querying Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
COOPIS '99 Proceedings of the Fourth IECIS International Conference on Cooperative Information Systems
Data warehouse scenarios for model management
ER'00 Proceedings of the 19th international conference on Conceptual modeling
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In this paper we propose a semi-automatic technique for deriving the similarity degree between two portions of heterogeneous, semi-structured information sources (hereafter, sub-sources). The proposed technique consists of two phases: the first one selects the most promising pairs of sub-sources, whereas the second one computes the similarity degree relative to each promising pair. In addition, we show that the detection of sub-source similarities is a special case (and a very interesting one, for semi-structured information sources) of the more general problem of Scheme Match. Finally we discuss some possible applications which can benefit of derived sub-source similarities. A real example case is presented for better clarifying the proposed technique.