Schema Evolution publication categorizer

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.