Knowledge Gathering and Matching in Heterogeneous Databases

In order to integrate a wide variety of databases or many diverse sources of information, we need the ability to learn the similarities directly from instances of the data, which may be embodied within a database model, a conceptual schema, application programs, or data contents. The process of determining semantically equivalent data items can not be "pre-programmed" since the information to be accessed is heterogeneous. Intelligent information integration involves extracting semantics, expressing them as metadata, and matching semantically equivalent data elements. Semint (SEMantic INTegrator) is a system prototype for semantic integration being developed at Northwestern University. It provides a graphical user interface and supports access to a variety of database systems and utilizes both schema information and data contents to determine attribute equivalence. In Semint, the knowledge of how to match equivalent data elements is "discovered", not "pre-programmed".