Schema Evolution publication categorizer

Querying semantically related data sources depends on the ability to map between their schemas. Unfortunately, in most cases matching between schema is still largely performed manually or semi-automatically. Consequently, the issue of finding semantic mappings became the principal bottleneck in the deployment of the mediation systems in large scale where the number of ontologies and or schemata to be put in correspondence is very large. Currently the mapping tools employ techniques for mapping two schemas at a time with human intervention for ensuring a good quality of mappings. In the large-scale scenario such techniques are not suitable. Indeed, in such a scenario one requires an automated performance oriented solution. Moreover, the automated method should also provide acceptable quality of mappings. In this paper, we present an automatic schema matching approach dealing with two aspects: performance and quality of mappings. However, we will focus on the performance aspect. For this, our method uses a B-tree index structure. Furthermore, our approach has been implemented and the experiments with real sets of schema show that it is scalable and provides very good performance.