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https://doi.org/10.1007/115757...
Part of book or chapter of book . 2005 . Peer-reviewed
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Semantic Schema Matching

Authors: Giunchiglia, Fausto; Shvaiko, Pavel; Yatskevich, Mikalai;

Semantic Schema Matching

Abstract

We view match as an operator that takes two graph-like structures (e.g., XML schemas) and produces a mapping between the nodes of these graphs that correspond semantically to each other. Semantic schema matching is based on the two ideas: (i) we discover mappings by computing semantic relations (e.g., equivalence, more general); (ii) we determine semantic relations by analyzing the meaning (concepts, not labels) which is codified in the elements and the structures of schemas. In this paper we present basic and optimized algorithms for semantic schema matching, and we discuss their implementation within the S-Match system. We also validate the approach and evaluate S-Match against three state of the art matching systems. The results look promising, in particular for what concerns quality and performance.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
47
Top 10%
Top 10%
Top 10%
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