Measuring semantic relatedness using wikipedia signed network

Wen Teng Yang, Hung-Yu Kao

Research output: Contribution to journalArticle

Abstract

Identifying the semantic relatedness of two words is an important task for the information retrieval, natural language processing, and text mining. However, due to the diversity of meaning for a word, the semantic relatedness of two words is still hard to precisely evaluate under the limited corpora. Nowadays, Wikipedia is now a huge and wiki-based encyclopedia on the internet that has become a valuable resource for research work. Wikipedia articles, written by a live collaboration of user editors, contain a high volume of reference links, URL identification for concepts and a complete revision history. Moreover, each Wikipedia article represents an individual concept that simultaneously contains other concepts that are hyperlinks of other articles embedded in its content. Through this, we believe that the semantic relatedness between two words can be found through the semantic relatedness between two Wikipedia articles. Therefore, we propose an Editor-Contribution-based Rank (ECR) algorithm for ranking the concepts in the article's content through all revisions and take the ranked concepts as a vector representing the article. We classify four types of relationship in which the behavior of addition and deletion maps appropriate and inappropriate concepts. ECR also extend the concept semantics by the editor-concept network. ECR ranks those concepts depending on the mutual signed-reinforcement relationship between the concepts and the editors. The results reveal that our method leads to prominent performance improvement and increases the correlation coefficient by a factor ranging from 4% to 23% over previous methods that calculate the relatedness between two articles.

Original languageEnglish
Pages (from-to)615-630
Number of pages16
JournalJournal of Information Science and Engineering
Volume29
Issue number4
Publication statusPublished - 2013 Jul

Fingerprint

Wikipedia
Semantics
semantics
editor
Information retrieval
Websites
Reinforcement
Internet
Processing
information retrieval
reinforcement
ranking

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computational Theory and Mathematics
  • Library and Information Sciences

Cite this

@article{9189e86b57144301a89a1771a438bfbc,
title = "Measuring semantic relatedness using wikipedia signed network",
abstract = "Identifying the semantic relatedness of two words is an important task for the information retrieval, natural language processing, and text mining. However, due to the diversity of meaning for a word, the semantic relatedness of two words is still hard to precisely evaluate under the limited corpora. Nowadays, Wikipedia is now a huge and wiki-based encyclopedia on the internet that has become a valuable resource for research work. Wikipedia articles, written by a live collaboration of user editors, contain a high volume of reference links, URL identification for concepts and a complete revision history. Moreover, each Wikipedia article represents an individual concept that simultaneously contains other concepts that are hyperlinks of other articles embedded in its content. Through this, we believe that the semantic relatedness between two words can be found through the semantic relatedness between two Wikipedia articles. Therefore, we propose an Editor-Contribution-based Rank (ECR) algorithm for ranking the concepts in the article's content through all revisions and take the ranked concepts as a vector representing the article. We classify four types of relationship in which the behavior of addition and deletion maps appropriate and inappropriate concepts. ECR also extend the concept semantics by the editor-concept network. ECR ranks those concepts depending on the mutual signed-reinforcement relationship between the concepts and the editors. The results reveal that our method leads to prominent performance improvement and increases the correlation coefficient by a factor ranging from 4{\%} to 23{\%} over previous methods that calculate the relatedness between two articles.",
author = "Yang, {Wen Teng} and Hung-Yu Kao",
year = "2013",
month = "7",
language = "English",
volume = "29",
pages = "615--630",
journal = "Journal of Information Science and Engineering",
issn = "1016-2364",
publisher = "Institute of Information Science",
number = "4",

}

Measuring semantic relatedness using wikipedia signed network. / Yang, Wen Teng; Kao, Hung-Yu.

In: Journal of Information Science and Engineering, Vol. 29, No. 4, 07.2013, p. 615-630.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Measuring semantic relatedness using wikipedia signed network

AU - Yang, Wen Teng

AU - Kao, Hung-Yu

PY - 2013/7

Y1 - 2013/7

N2 - Identifying the semantic relatedness of two words is an important task for the information retrieval, natural language processing, and text mining. However, due to the diversity of meaning for a word, the semantic relatedness of two words is still hard to precisely evaluate under the limited corpora. Nowadays, Wikipedia is now a huge and wiki-based encyclopedia on the internet that has become a valuable resource for research work. Wikipedia articles, written by a live collaboration of user editors, contain a high volume of reference links, URL identification for concepts and a complete revision history. Moreover, each Wikipedia article represents an individual concept that simultaneously contains other concepts that are hyperlinks of other articles embedded in its content. Through this, we believe that the semantic relatedness between two words can be found through the semantic relatedness between two Wikipedia articles. Therefore, we propose an Editor-Contribution-based Rank (ECR) algorithm for ranking the concepts in the article's content through all revisions and take the ranked concepts as a vector representing the article. We classify four types of relationship in which the behavior of addition and deletion maps appropriate and inappropriate concepts. ECR also extend the concept semantics by the editor-concept network. ECR ranks those concepts depending on the mutual signed-reinforcement relationship between the concepts and the editors. The results reveal that our method leads to prominent performance improvement and increases the correlation coefficient by a factor ranging from 4% to 23% over previous methods that calculate the relatedness between two articles.

AB - Identifying the semantic relatedness of two words is an important task for the information retrieval, natural language processing, and text mining. However, due to the diversity of meaning for a word, the semantic relatedness of two words is still hard to precisely evaluate under the limited corpora. Nowadays, Wikipedia is now a huge and wiki-based encyclopedia on the internet that has become a valuable resource for research work. Wikipedia articles, written by a live collaboration of user editors, contain a high volume of reference links, URL identification for concepts and a complete revision history. Moreover, each Wikipedia article represents an individual concept that simultaneously contains other concepts that are hyperlinks of other articles embedded in its content. Through this, we believe that the semantic relatedness between two words can be found through the semantic relatedness between two Wikipedia articles. Therefore, we propose an Editor-Contribution-based Rank (ECR) algorithm for ranking the concepts in the article's content through all revisions and take the ranked concepts as a vector representing the article. We classify four types of relationship in which the behavior of addition and deletion maps appropriate and inappropriate concepts. ECR also extend the concept semantics by the editor-concept network. ECR ranks those concepts depending on the mutual signed-reinforcement relationship between the concepts and the editors. The results reveal that our method leads to prominent performance improvement and increases the correlation coefficient by a factor ranging from 4% to 23% over previous methods that calculate the relatedness between two articles.

UR - http://www.scopus.com/inward/record.url?scp=84878800764&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84878800764&partnerID=8YFLogxK

M3 - Article

VL - 29

SP - 615

EP - 630

JO - Journal of Information Science and Engineering

JF - Journal of Information Science and Engineering

SN - 1016-2364

IS - 4

ER -