SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks

✍ Scribed by Wen-Syan Li; Chris Clifton

• Publisher: Elsevier Science
• Year: 2000
• Tongue: English
• Weight: 735 KB
• Volume: 33
• Category: Article
• ISSN: 0169-023X
• DOI: 10.1016/s0169-023x(99)00044-0

✦ Synopsis

One step in interoperating among heterogeneous databases is semantic integration: Identifying relationships between attributes or classes in dierent database schemas. SEMantic INTegrator (SEMINT) is a tool based on neural networks to assist in identifying attribute correspondences in heterogeneous databases. SEMINT supports access to a variety of database systems and utilizes both schema information and data contents to produce rules for matching corresponding attributes automatically. This paper provides theoretical background and implementation details of SEMINT. Experimental results from large and complex real databases are presented. We discuss the eectiveness of SEMINT and our experiences with attribute correspondence identi®cation in various environments.