article

Matching large XML schemas

Authors:

Sabine MaßmannAuthors Info & Claims

ACM SIGMOD Record, Volume 33, Issue 4

Pages 26 - 31

https://doi.org/10.1145/1041410.1041415

Published: 01 December 2004 Publication History

Abstract

Current schema matching approaches still have to improve for very large and complex schemas. Such schemas are increasingly written in the standard language W3C XML schema, especially in E-business applications. The high expressive power and versatility of this schema language, in particular its type system and support for distributed schemas and name-spaces, introduce new issues. In this paper, we study some of the important problems in matching such large XML schemas. We propose a fragment-oriented match approach to decompose a large match problem into several smaller ones and to reuse previous match results at the level of schema fragments.

References

[1]

Aamodt, A., Plaza, E.: Case-based Reasoning - Foundational Issues, Methodological Variations, and System Approaches. AI Communications 7: 1, 1994]]

Digital Library

[2]

Bergamaschi, S. et al.: Semantic Integration of Semistructured and Structured Data Sources. ACM SIGMOD Record 28: 1, 1999]]

Digital Library

[3]

Berlin, J., A. Motro: Autoplex: Automated Discovery of Content for Virtual Databases. CoopIS 2001]]

Digital Library

[4]

Bernstein, P. A. et al: Industrial-strength Schema Matching. ACM SIGMOD Record, 2004 (this issue)]]

Digital Library

[5]

Do, H. H., E. Rahm: COMA - A System for Flexible Combination of Match Algorithms. VLDB 2002]]

Digital Library

[6]

Do, H. H., S. Melnik, E. Rahm: Comparison of Schema Matching Evaluations. GI-Workshop Web and Databases, LNCS 2593, 2003]]

Digital Library

[7]

Doan, A. H. et al.: Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach. SIGMOD 2001]]

Digital Library

[8]

Li, W., C. Clifton: SemInt - A Tool for Identifying Attribute Correspondences in Heterogeneous Databases Using Neural Network. Data and Knowledge Engineering 33:1, 2000]]

Digital Library

[9]

Madhavan, J. et al.: Corpus-based Schema Matching. Workshop on Information Integration on the Web (IIWeb), 2003]]

[10]

Madhavan, J., P. A. Bernstein, E. Rahm: Generic Schema Matching with Cupid. VLDB 2001]]

Digital Library

[11]

Melnik, S., H. Garcia-Molina, E. Rahm: Similarity Flooding - A Versatile Graph Matching Algorithm. ICDE 2002]]

Digital Library

[12]

Mork, P., P. A. Bernstein: Adapting a Generic Match Algorithm to Align Ontologies of Human Anatomy. ICDE 2004]]

Digital Library

[13]

Naumann, F. et al.: Attribute Classification Using Feature Analysis. ICDE 2002]]

[14]

Palopoli, L. et al.: Uniform Techniques for Deriving Similarities of Objects and Subschemes in Heterogeneous Databases. IEEE Trans. Knowl. Data Eng. 15: 2, 2003]]

Digital Library

[15]

Rahm, E., P. A. Bernstein: A Survey of Approaches to Automatic Schema Matching. VLDB Journal 10: 4, 2001]]

Digital Library

[16]

XML Schemas - Best Practices. www.xfront.com/BestPracticesHomepage.html]]

Cited By

Wang* CLi YChen J(2023)Text mining and knowledge graph construction from geoscience literature legacy: A reviewRecent Advancement in Geoinformatics and Data Science10.1130/2022.2558(02)(11-28)Online publication date: 22-Mar-2023
https://doi.org/10.1130/2022.2558(02)
Martinez-Mosquera DNavarrete RLuján-Mora S(2021)Efficient processing of complex XSD using Hive and SparkPeerJ Computer Science10.7717/peerj-cs.6527(e652)Online publication date: 17-Aug-2021
https://doi.org/10.7717/peerj-cs.652
Hacherouf MNait-Bahloul SCruz C(2019)Transforming XML schemas into OWL ontologies using formal concept analysisSoftware and Systems Modeling (SoSyM)10.1007/s10270-017-0651-418:3(2093-2110)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10270-017-0651-4
Show More Cited By

Recommendations

Conceptual modeling of XML schemas
WIDM '03: Proceedings of the 5th ACM international workshop on Web information and data management

XML has become the standard format for representing structured and semi-structured data on the Web. To describe the structure and content of XML data, several XML schema languages have been proposed. Although being very useful for validating XML ...
XML Schemas
Mapping Relational Schemas to XML DTDs with Constraints
IMSCCS '06: Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 2 (IMSCCS'06) - Volume 02

XML is becoming a prevalent format and de facto standard for data exchange in many applications. While traditionally, lots of data are stored and managed in relational databases. There is an urgent need to research some efficient methods to convert ...

Comments

Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 33, Issue 4

December 2004

92 pages

ISSN:0163-5808

DOI:10.1145/1041410

Issue’s Table of Contents

Copyright © 2004 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2004

Published in SIGMOD Volume 33, Issue 4

Check for updates

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

78
Total Citations
View Citations
854
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang* CLi YChen J(2023)Text mining and knowledge graph construction from geoscience literature legacy: A reviewRecent Advancement in Geoinformatics and Data Science10.1130/2022.2558(02)(11-28)Online publication date: 22-Mar-2023
https://doi.org/10.1130/2022.2558(02)
Martinez-Mosquera DNavarrete RLuján-Mora S(2021)Efficient processing of complex XSD using Hive and SparkPeerJ Computer Science10.7717/peerj-cs.6527(e652)Online publication date: 17-Aug-2021
https://doi.org/10.7717/peerj-cs.652
Hacherouf MNait-Bahloul SCruz C(2019)Transforming XML schemas into OWL ontologies using formal concept analysisSoftware and Systems Modeling (SoSyM)10.1007/s10270-017-0651-418:3(2093-2110)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10270-017-0651-4
Athanasopoulos DZarras A(2018)Mining Abstract XML Data-TypesACM Transactions on the Web10.1145/326746713:1(1-37)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1145/3267467
Idoudi RSaheb Ettabaa KSolaiman BHamrouni K(2018)Ontology knowledge mining for ontology conceptual enrichmentKnowledge Management Research & Practice10.1080/14778238.2018.1538599(1-10)Online publication date: 30-Nov-2018
https://doi.org/10.1080/14778238.2018.1538599
Ochieng PKyanda S(2018)A K-way spectral partitioning of an ontology for ontology matchingDistributed and Parallel Databases10.1007/s10619-018-7222-836:4(643-673)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10619-018-7222-8
El Yahyaoui El Idrissi SZellou AIdri A(2017)A New Mapping Approach between XML Schemas in a P2P Environment2017 10th International Conference on Developments in eSystems Engineering (DeSE)10.1109/DeSE.2017.16(212-217)Online publication date: Jun-2017
https://doi.org/10.1109/DeSE.2017.16
Tan SHoon G(2016)An efficient similarity matching for clustering XML element2016 Third International Conference on Information Retrieval and Knowledge Management (CAMP)10.1109/INFRKM.2016.7806343(101-106)Online publication date: Aug-2016
https://doi.org/10.1109/INFRKM.2016.7806343
Wang LMurayama H(2015)A multi‐version CIM‐based database platform for smart gridIEEJ Transactions on Electrical and Electronic Engineering10.1002/tee.2208910:3(330-339)Online publication date: 18-Feb-2015
https://doi.org/10.1002/tee.22089
Zolfaghari VJalali M(2014)Scalable ontology matching2014 Iranian Conference on Intelligent Systems (ICIS)10.1109/IranianCIS.2014.6802549(1-4)Online publication date: Feb-2014
https://doi.org/10.1109/IranianCIS.2014.6802549
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents