Semantic Integration in Heterogeneous Databases Using Neural Networks

Abstract

One important step in integrating heteroge-neous databases is matching equivalent at-tributes: Determining which fields in two databases refer to the same data. The mean-ing of information may be embodied within a. database model, a conceptual schema, appli-cation programs, or data contents. Integra-tion involves extracting semantics, expressing them as metadata, and matching semantically equivalent data elements. We present a proce-dure using a classifier to categorize attributes according to their field specifications and data values, then train a neural network to recog-nize similar attributes. In our technique, the knowledge of how to match equivalent data elements is “discovered ” from metadata, not “pre-programmed”. 1

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