Difference between revisions of "Semantic Relatedness Measurement based on Wikipedia Link Co‐Occurrence Analysis"

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{{Infobox work
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| title = Semantic Relatedness Measurement based on Wikipedia Link Co‐Occurrence Analysis
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| date = 2011
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| authors = [[Masahiro Ito]]<br />[[Kotaro Nakayama]]<br />[[Takahiro Hara]]<br />[[Shojiro Nishio]]
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| doi = 10.1108/17440081111125653
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| link = https://www.emeraldinsight.com/doi/abs/10.1108/17440081111125653
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}}
 
'''Semantic Relatedness Measurement based on Wikipedia Link Co‐Occurrence Analysis''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Masahiro Ito]], [[Kotaro Nakayama]], [[Takahiro Hara]] and [[Shojiro Nishio]].
 
'''Semantic Relatedness Measurement based on Wikipedia Link Co‐Occurrence Analysis''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Masahiro Ito]], [[Kotaro Nakayama]], [[Takahiro Hara]] and [[Shojiro Nishio]].
  
 
== Overview ==
 
== Overview ==
 
Purpose – Recently, the importance and effectiveness of [[Wikipedia]] Mining has been shown in several researches. One popular research area on Wikipedia Mining focuses on semantic [[relatedness]] measurement, and research in this area has shown that Wikipedia can be used for semantic relatedness measurement. However, previous methods are facing two problems; accuracy and scalability. To solve these problems, the purpose of this paper is to propose an efficient semantic relatedness measurement method that leverages global statistical information of Wikipedia. Furthermore, a new test collection is constructed based on Wikipedia concepts for evaluating semantic relatedness measurement methods.Design/methodology/approach – The authors' approach leverages global statistical information of the whole Wikipedia to compute semantic relatedness among concepts (disambiguated terms) by analyzing co‐occurrences of link pairs in all Wikipedia articles. In Wikipedia, an article represents a concept and a link to another articl...
 
Purpose – Recently, the importance and effectiveness of [[Wikipedia]] Mining has been shown in several researches. One popular research area on Wikipedia Mining focuses on semantic [[relatedness]] measurement, and research in this area has shown that Wikipedia can be used for semantic relatedness measurement. However, previous methods are facing two problems; accuracy and scalability. To solve these problems, the purpose of this paper is to propose an efficient semantic relatedness measurement method that leverages global statistical information of Wikipedia. Furthermore, a new test collection is constructed based on Wikipedia concepts for evaluating semantic relatedness measurement methods.Design/methodology/approach – The authors' approach leverages global statistical information of the whole Wikipedia to compute semantic relatedness among concepts (disambiguated terms) by analyzing co‐occurrences of link pairs in all Wikipedia articles. In Wikipedia, an article represents a concept and a link to another articl...

Latest revision as of 09:32, 11 February 2020


Semantic Relatedness Measurement based on Wikipedia Link Co‐Occurrence Analysis
Authors
Masahiro Ito
Kotaro Nakayama
Takahiro Hara
Shojiro Nishio
Publication date
2011
DOI
10.1108/17440081111125653
Links
Original

Semantic Relatedness Measurement based on Wikipedia Link Co‐Occurrence Analysis - scientific work related to Wikipedia quality published in 2011, written by Masahiro Ito, Kotaro Nakayama, Takahiro Hara and Shojiro Nishio.

Overview

Purpose – Recently, the importance and effectiveness of Wikipedia Mining has been shown in several researches. One popular research area on Wikipedia Mining focuses on semantic relatedness measurement, and research in this area has shown that Wikipedia can be used for semantic relatedness measurement. However, previous methods are facing two problems; accuracy and scalability. To solve these problems, the purpose of this paper is to propose an efficient semantic relatedness measurement method that leverages global statistical information of Wikipedia. Furthermore, a new test collection is constructed based on Wikipedia concepts for evaluating semantic relatedness measurement methods.Design/methodology/approach – The authors' approach leverages global statistical information of the whole Wikipedia to compute semantic relatedness among concepts (disambiguated terms) by analyzing co‐occurrences of link pairs in all Wikipedia articles. In Wikipedia, an article represents a concept and a link to another articl...