Article

Using proportional transportation similarity with learned element semantics for XML document clustering

Authors:

Xiaojun Wan,

Jianwu YangAuthors Info & Claims

WWW '06: Proceedings of the 15th international conference on World Wide Web

Pages 961 - 962

https://doi.org/10.1145/1135777.1135965

Published: 23 May 2006 Publication History

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Abstract

This paper proposes a novel approach to measuring XML document similarity by taking into account the semantics between XML elements. The motivation of the proposed approach is to overcome the problems of "under-contributionö and "over-contributionö existing in previous work. The element semantics are learned in an unsupervised way and the Proportional Transportation Similarity is proposed to evaluate XML document similarity by modeling the similarity calculation as a transportation problem. Experiments of clustering are performed on three ACM SIGMOD data sets and results show the favorable performance of the proposed approach.

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Cited By

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Faisal SSarwar M(2019)Temporal and multi-versioned XML documentsInformation Processing and Management: an International Journal10.1016/j.ipm.2013.08.00350:1(113-131)Online publication date: 25-Nov-2019
https://dl.acm.org/doi/10.1016/j.ipm.2013.08.003
Thulasi ARemya KRaju G(2017)Structure based XML document clustering: A review2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS)10.1109/ICTUS.2017.8286068(543-547)Online publication date: Dec-2017
https://doi.org/10.1109/ICTUS.2017.8286068
de Brum Saccol DEdelweiss Nde Matos Galante RZaniolo CKing PSimske S(2007)XML version detectionProceedings of the 2007 ACM symposium on Document engineering10.1145/1284420.1284441(79-88)Online publication date: 28-Aug-2007
https://dl.acm.org/doi/10.1145/1284420.1284441

Index Terms

Using proportional transportation similarity with learned element semantics for XML document clustering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Information & Contributors

Information

Published In

WWW '06: Proceedings of the 15th international conference on World Wide Web

May 2006

1102 pages

ISBN:1595933239

DOI:10.1145/1135777

General Chairs:
Leslie Carr
University of Southampton
,
David De Roure
University of Southampton
,
Arun Iyengar
IBM Research
,
Program Chairs:
Carole Goble
University of Manchester, UK
,
Mike Dahlin
University of Texas at Austin

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 May 2006

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WWW06

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WWW06: The 15th International World Wide Web Conference 2006

May 23 - 26, 2006

Edinburgh, Scotland

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Faisal SSarwar M(2019)Temporal and multi-versioned XML documentsInformation Processing and Management: an International Journal10.1016/j.ipm.2013.08.00350:1(113-131)Online publication date: 25-Nov-2019
https://dl.acm.org/doi/10.1016/j.ipm.2013.08.003
Thulasi ARemya KRaju G(2017)Structure based XML document clustering: A review2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS)10.1109/ICTUS.2017.8286068(543-547)Online publication date: Dec-2017
https://doi.org/10.1109/ICTUS.2017.8286068
de Brum Saccol DEdelweiss Nde Matos Galante RZaniolo CKing PSimske S(2007)XML version detectionProceedings of the 2007 ACM symposium on Document engineering10.1145/1284420.1284441(79-88)Online publication date: 28-Aug-2007
https://dl.acm.org/doi/10.1145/1284420.1284441

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Integrating Element and Term Semantics for Similarity-Based XML Document Clustering

XML Document Clustering Using Common XPath