Schema Evolution publication categorizer

A broad spectrum of data is available on the Web in distinct heterogeneous sources, and stored under different formats. As the num- ber of systems that utilize this heterogeneous data grows, the importance of data translation and conversion mechanisms increases greatly. In this paper we present a new translation system, based on schema-matching, aimed at simplifying the intricate task of data conver- sion. We observe that in many cases the schema of the data in the source system is very similar to that of the target system. In such cases, much of the translation work can be done automatically, based on the schemas sim- ilarity. This saves a lot of effort for the user, limiting the amount of programming needed. We define common schema and data mod- els, in which schemas and data (resp.) from many common models can be represented. Us- ing a rule-based method, the source schema is compared with the target one, and each component in the source schema is matched with a corresponding component in the tar- get schema. Then, based on the matching achieved, data instances of the source schema can be translated to instances of the target schema. We show that our schema-based translation system allows a convenient specifi- cation and customization of data conversions, and can be easily combined with the tradi- tional data-based translation languages.