Proceedings of the 2016 International Conference on Education, Management, Computer and Society

A Hybrid Method to Evaluate Similarity of XML Document

Authors
Yubiao Dai, Xueli Ren
Corresponding Author
Yubiao Dai
Available Online January 2016.
DOI
10.2991/emcs-16.2016.165How to use a DOI?
Keywords
ML; Path; Semantic; Fuzzy Longest common subsequence; Hungarian
Abstract

XML is an important standard of information representation and data exchange over the Internet, document classification is an important way to get useful information from the mass of information solutions, a method of XML document classification is proposed based on fuzzy matching path in the paper. First, the information that has no influence on the classification is removed; Then a mixed method is used to compute XML document similarity, XML document is expressed as a collection of path, deleting the recurring and matching fuzzy path in order to improve efficiency, Hungarian algorithm to calculate the similarity between documents; Finally, 2 experiments are done and the results show that the method is effective.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Education, Management, Computer and Society
Series
Advances in Computer Science Research
Publication Date
January 2016
ISBN
10.2991/emcs-16.2016.165
ISSN
2352-538X
DOI
10.2991/emcs-16.2016.165How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yubiao Dai
AU  - Xueli Ren
PY  - 2016/01
DA  - 2016/01
TI  - A Hybrid Method to Evaluate Similarity of XML Document
BT  - Proceedings of the 2016 International Conference on Education, Management, Computer and Society
PB  - Atlantis Press
SP  - 677
EP  - 680
SN  - 2352-538X
UR  - https://doi.org/10.2991/emcs-16.2016.165
DO  - 10.2991/emcs-16.2016.165
ID  - Dai2016/01
ER  -