One step in interoperating among heterogeneous databases is semantic integration: Identifying relationships between attributes or classes in different 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 effectiveness of SEMINT and our experiences with attribute correspondence identification in various environments.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 15 articles , 1 standard article )

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  1. Rubiolo, M.; Caliusco, M. L.; Stegmayer, G.; Coronel, M.; Gareli Fabrizi, M.: Knowledge discovery through ontology matching: an approach based on an artificial neural network model (2012) ioport
  2. Gal, Avigdor; Sagi, Tomer: Tuning the ensemble selection process of schema matchers (2010) ioport
  3. Unal, Ozgul; Afsarmanesh, Hamideh: Semi-automated schema integration with SASMINT (2010) ioport
  4. Evermann, Joerg: Theories of meaning in schema matching: an exploratory study (2009) ioport
  5. Fletcher, George H. L.; Wyss, Catharine M.: Towards a general framework for effective solutions to the data mapping problem (2009)
  6. Zhang, Zhi; Shi, Pengfei; Che, Haoyang; Gu, Jun: An algebraic framework for schema matching (2008)
  7. Barbançon, Francois; Miranker, Daniel P.: SPHINX: Schema integration by example (2007) ioport
  8. De Meo, Pasquale; Quattrone, Giovanni; Ursino, Domenico; Terracina, Giorgio: An approach to extracting interschema properties from XML schemas at various “severity” levels (2007)
  9. Koeller, Andreas; Rundensteiner, Elke A.: Heuristic strategies for the discovery of inclusion dependencies and other patterns (2006)
  10. Huma, Zille; Rehman, Muhammad Jaffar-Ur; Iftikhar, Nadeem: An ontology-based framework for semi-automatic schema integration (2005) ioport
  11. Melnik, Sergey: Generic model management. Concepts and algorithms. (2004)
  12. Doan, AnHai; Domingos, Pedro; Halevy, Alon: Learning to match the schemas of data sources: A multistrategy approach (2003)
  13. Berlin, Jacob; Motro, Amihai: Database schema matching using machine learning with feature selection (2002)
  14. Rahm, Erhard; Bernstein, Philip A.: A survey of approaches to automatic schema matching (2001)
  15. Li, W.-S.; Clifton, C.: SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks (2000)