Original Contribution: Stacked generalization
Neural Networks
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
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
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Constructing virtual documents for ontology matching
Proceedings of the 15th international conference on World Wide Web
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Contextualized Linguistic Matching for Heterogeneous Data Source Integration
MCETECH '08 Proceedings of the 2008 International MCETECH Conference on e-Technologies
Issues in stacked generalization
Journal of Artificial Intelligence Research
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Semantic mapping is a fundamental step towards application interoperability, data integration and service oriented computing over the Internet. It consists in matching semantically equivalent concepts coming from heterogeneous data sources. This basic task is nevertheless tedious and often error prone if handled manually. Therefore, many systems have been developed for its automation. However, virtually all solutions currently target a specific type of applications and rely on rigid approaches applying, without distinction, the same matching technique with the same fixed procedures on data to be aligned without regard for their characteristics. The problem is that such mapping systems are not adapted to the diversity of Internet where data sources and domains are numerous, highly heterogeneous and often changing. This article presents INDIGO, an adaptive mapping solution which takes into account the diversified nature of data sources that are shared over the Internet. It provides multiple matching strategies which can be flexibly combined to take into consideration the specificities of the data sources being aligned.