Semantic Integration in Heterogeneous Databases Using Neural Networks
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
iMAP: discovering complex semantic matches between database schemas
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
Improving XML schema matching performance using Prüfer sequences
Data & Knowledge Engineering
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The elaboration of semantic matching between hetero geneous data sources is a fundamental step in the design of data sharing applications. This task is tedious and often error prone if handled manually. Therefore, many systems have been developed for its automation. But, the majority of them focus on the problem of finding simple (one-to-one) matching. This is likely due to the fact that complex (many-tomany) matching raises a far more difficult problem since the search space of concept combinations can be tremendously large. This article presents Indigo, a system which can compute complex matching by taking into account data sources' context. First, it enriches data sources with complex concepts extracted from their respective development artifacts. It then computes a mapping between the two data sources thus enhanced.