Schema Integration Based on Uncertain Semantic Mappings

Schema integration is the activity of providing a unified representation of multiple data sources. The core problems in schema integration are: schema matching, i.e. the identification of correspondences, or mappings, between schema objects, and schema merging, i.e. the creation of a unified schema based on the identified mappings. Existing schema matching approaches attempt to identify a single mapping between each pair of objects, for which they are 100% certain of its correctness. However, this is impossible in general, thus a human expert always has to validate or modify it. In this paper, we propose a new schema integration approach where the uncertainty in the identified mappings that is inherent in the schema matching process is explicitly represented, and that uncertainty propagates to the schema merging process, and finally it is depicted in the resulting integrated schema.

[1]  DoanAnHai,et al.  Learning to match ontologies on the Semantic Web , 2003, VLDB 2003.

[2]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[3]  Maurice van Keulen,et al.  A probabilistic XML approach to data integration , 2005, 21st International Conference on Data Engineering (ICDE'05).

[4]  Peter McBrien,et al.  A General Approach to the Generation of Conceptual Model Transformations , 2005, CAiSE.

[5]  Alexandra Poulovassilis,et al.  Data integration by bi-directional schema transformation rules , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[6]  Pedro M. Domingos,et al.  Learning to map between ontologies on the semantic web , 2002, WWW '02.

[7]  Erhard Rahm,et al.  Rondo: a programming platform for generic model management , 2003, SIGMOD '03.

[8]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[9]  Erhard Rahm,et al.  Generic Schema Matching with Cupid , 2001, VLDB.

[10]  Philip A. Bernstein,et al.  Applying Model Management to Classical Meta Data Problems , 2003, CIDR.

[11]  Nikos Rizopoulos Automatic Discovery of Semantic Relationships Between Schema Elements , 2004, ICEIS.

[12]  Philip A. Bernstein,et al.  Merging Models Based on Given Correspondences , 2003, VLDB.

[13]  Avigdor Gal,et al.  A framework for modeling and evaluating automatic semantic reconciliation , 2005, The VLDB Journal.

[14]  Stefano Spaccapietra,et al.  View Integration: A Step Forward in Solving Structural Conflicts , 1994, IEEE Trans. Knowl. Data Eng..

[15]  Erhard Rahm,et al.  A survey of approaches to automatic schema matching , 2001, The VLDB Journal.

[16]  Stephen Hayne,et al.  Multi-user view integration system (MUVIS): an expert system for view integration , 1990, [1990] Proceedings. Sixth International Conference on Data Engineering.