Abstract
One important step in integrating heterogeneous databases is matching equivalent attributes: Determining which fields in two databases refer to the same data. The meaning of information may be embodied within a.
database model, a conceptual schema, application programs, or data contents. Integration involves extracting semantics, expressing
them as metadata, and matching semantically equivalent data elements. We present a procedure using a classifier to categorize attributes
according to their field specifications and data values, then train a neural network to recognize similar attributes. In our technique, the knowledge of how to match equivalent data elements is “discovered†from metadata , not
“pre-programmedâ€.
Note
Proceedings of the 20th International Conference on Very Large Data Bases
September 12-15, 1994 in Santiago, Chile
