Schema matching plays a key role in many different
applications, such as schema integration, data integration, data
warehousing, data transformation, E-commerce, peer-to-peer data
management, ontology matching and integration, semantic Web,
semantic query processing, etc. Manual matching is expensive and
error-prone, so it is therefore important to develop techniques to
automate the schema matching process. In this paper, we present a
solution for XML schema automated matching problem which
produces semantic mappings between corresponding schema
elements of given source and target schemas. This solution
contributed in solving more comprehensively and efficiently XML
schema automated matching problem. Our solution based on
combining linguistic similarity, data type compatibility and structural
similarity of XML schema elements. After describing our solution,
we present experimental results that demonstrate the effectiveness of
this approach.
[1] E. Rahm and P.A. Bernstein. A survey of approaches to automatic
schema matching. In VLDB Journal, pages 10: 334-350, 2001.
[2] A. H. Doan. Learning to map between structured representations of
data. PhD thesis, University of Washington, 2002.
[3] L. Zamboulis. XML Schema Matching & XML Data Migration &
Integration: A Step Towards The Semantic Web Vision. Technical
Report, 2003.
[4] J. Madhavan, P. A. Bernstein, and E. Rahm. Generic schema matching
with Cupid. MSR Tech. Report MSR-TR-2001-58, 2001, Available at:
http://www.research.microsoft.com/pubs.
[5] S. Melnik, H. Garcia-Molina, E. Rahm. Similarity Flooding: A versatile
Graph Matching Algorithm and its Application to Schema Matching. In
Proceedings of the 18th International Conference on Data Engineering,
2002. Available at: http://dbpubs.stanford.edu/pub/2001-25. (Extended
Technical Report, 2001).
[6] H. H. Do and E. Rahm. COMA - a system for flexible combination of
schema matching approaches. In Proceedings of the Very Large Data
Bases Conference (VLDB), pages 610-621, 2001.
[7] A.G. Miller. WordNet: A lexical Database for English. In ACM 38 (11),
pages 39-41, 1995.
[8] A. Budanitsky and G. Hirst. Semantic distance in WordNet. An
experimental, application oriented evaluation of five measures, 2003.
[9] Lexical chains as representations of context for the detection and
correction of malapropisms. In: Christiane Fellbaum (editor), WordNet:
An electronic lexical database, Cambridge, MA: The MIT Press, 1998.
[10] D.Carmel, N. Efraty, G. M. Landau, Y. S. Maarek, and Y. Mass. An
Extension of the vector space model for querying XML documents via
XML fragments. Second Edition of the XML and IR Workshop, In
SIGIR Forum, Volume 36 Number 2, Fall 2002.
[11] L.Xu. Source Discovery and Schema Mapping for Data Integration, PhD
thesis, 2003.
[12] Mong Li Lee, Liang Huai Yang, Wynne Hsu, Xia Yang. XClust:
Clustering XML Schemas for Effective Integration, in 11th ACM
International Conference on Information and Knowledge Management
(CIKM), McLean, Virginia, November 2002.
[13] N. Routledge, L. Bird and A. Goodchild. UML and XML Schema,
ADC'2002, 2002.
[14] R. Xio, T. Dillon, E. Chang and L. Feng (2001). Modeling and
Transformation of Object Oriented Conceptual Models into XML
Schema. DEXA 2001, LNCS 2113, pages795-804, 2001.
[15] G. Cobena, S. Abiteboul, and A. Marian. Detecting changes in XML
Documents. In ICDE, 2002.
[16] D. Shasha, J. Wang, K. Zhang, and F. Shih. Fast algorithms for the unit
cost editing distance between trees. In Journal of Algorithms, pages 581-
621, 1990.
[17] S. Amer-Yahia, S. Cho, D. Srivastava, "Tree Pattern Relaxation"
EDBT'02, 2002.
[18] E.W. Myers. Incremental alignment algorithms and their applications.
TR 86-22, Department of Computer Science, University of Arizona,
1986.
[19] A. Boukottaya, C. Vanoirbeek. Schema Matching for Transforming
Structured Documents. In DocEng'05, 2-4, 2005.
[20] XML Schema Part 0: Primer, W3C Recommendation, 2004. Available
at: http://www.w3.org/TR/xmlschema-0/.
[21] XML Schema Part 1: Structures, W3C Recommendation, 2004.
Available at: http://www.w3.org/TR/xmlschema-1/.
[22] XML Schema Part 2: Datatypes, W3C Recommendation 2004. Available
at: http://www.w3.org/TR/xmlschema-2/.
[1] E. Rahm and P.A. Bernstein. A survey of approaches to automatic
schema matching. In VLDB Journal, pages 10: 334-350, 2001.
[2] A. H. Doan. Learning to map between structured representations of
data. PhD thesis, University of Washington, 2002.
[3] L. Zamboulis. XML Schema Matching & XML Data Migration &
Integration: A Step Towards The Semantic Web Vision. Technical
Report, 2003.
[4] J. Madhavan, P. A. Bernstein, and E. Rahm. Generic schema matching
with Cupid. MSR Tech. Report MSR-TR-2001-58, 2001, Available at:
http://www.research.microsoft.com/pubs.
[5] S. Melnik, H. Garcia-Molina, E. Rahm. Similarity Flooding: A versatile
Graph Matching Algorithm and its Application to Schema Matching. In
Proceedings of the 18th International Conference on Data Engineering,
2002. Available at: http://dbpubs.stanford.edu/pub/2001-25. (Extended
Technical Report, 2001).
[6] H. H. Do and E. Rahm. COMA - a system for flexible combination of
schema matching approaches. In Proceedings of the Very Large Data
Bases Conference (VLDB), pages 610-621, 2001.
[7] A.G. Miller. WordNet: A lexical Database for English. In ACM 38 (11),
pages 39-41, 1995.
[8] A. Budanitsky and G. Hirst. Semantic distance in WordNet. An
experimental, application oriented evaluation of five measures, 2003.
[9] Lexical chains as representations of context for the detection and
correction of malapropisms. In: Christiane Fellbaum (editor), WordNet:
An electronic lexical database, Cambridge, MA: The MIT Press, 1998.
[10] D.Carmel, N. Efraty, G. M. Landau, Y. S. Maarek, and Y. Mass. An
Extension of the vector space model for querying XML documents via
XML fragments. Second Edition of the XML and IR Workshop, In
SIGIR Forum, Volume 36 Number 2, Fall 2002.
[11] L.Xu. Source Discovery and Schema Mapping for Data Integration, PhD
thesis, 2003.
[12] Mong Li Lee, Liang Huai Yang, Wynne Hsu, Xia Yang. XClust:
Clustering XML Schemas for Effective Integration, in 11th ACM
International Conference on Information and Knowledge Management
(CIKM), McLean, Virginia, November 2002.
[13] N. Routledge, L. Bird and A. Goodchild. UML and XML Schema,
ADC'2002, 2002.
[14] R. Xio, T. Dillon, E. Chang and L. Feng (2001). Modeling and
Transformation of Object Oriented Conceptual Models into XML
Schema. DEXA 2001, LNCS 2113, pages795-804, 2001.
[15] G. Cobena, S. Abiteboul, and A. Marian. Detecting changes in XML
Documents. In ICDE, 2002.
[16] D. Shasha, J. Wang, K. Zhang, and F. Shih. Fast algorithms for the unit
cost editing distance between trees. In Journal of Algorithms, pages 581-
621, 1990.
[17] S. Amer-Yahia, S. Cho, D. Srivastava, "Tree Pattern Relaxation"
EDBT'02, 2002.
[18] E.W. Myers. Incremental alignment algorithms and their applications.
TR 86-22, Department of Computer Science, University of Arizona,
1986.
[19] A. Boukottaya, C. Vanoirbeek. Schema Matching for Transforming
Structured Documents. In DocEng'05, 2-4, 2005.
[20] XML Schema Part 0: Primer, W3C Recommendation, 2004. Available
at: http://www.w3.org/TR/xmlschema-0/.
[21] XML Schema Part 1: Structures, W3C Recommendation, 2004.
Available at: http://www.w3.org/TR/xmlschema-1/.
[22] XML Schema Part 2: Datatypes, W3C Recommendation 2004. Available
at: http://www.w3.org/TR/xmlschema-2/.
@article{"International Journal of Information, Control and Computer Sciences:52508", author = "Huynh Quyet Thang and Vo Sy Nam", title = "XML Schema Automatic Matching Solution", abstract = "Schema matching plays a key role in many different
applications, such as schema integration, data integration, data
warehousing, data transformation, E-commerce, peer-to-peer data
management, ontology matching and integration, semantic Web,
semantic query processing, etc. Manual matching is expensive and
error-prone, so it is therefore important to develop techniques to
automate the schema matching process. In this paper, we present a
solution for XML schema automated matching problem which
produces semantic mappings between corresponding schema
elements of given source and target schemas. This solution
contributed in solving more comprehensively and efficiently XML
schema automated matching problem. Our solution based on
combining linguistic similarity, data type compatibility and structural
similarity of XML schema elements. After describing our solution,
we present experimental results that demonstrate the effectiveness of
this approach.", keywords = "XML Schema, Schema Matching, SemanticMatching, Automatic XML Schema Matching.", volume = "4", number = "3", pages = "398-7", }
