Difference between revisions of "Wnavis: Navigating Wikipedia Semantically with an Sna-Based Summarization Technique"

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== Overview ==
 
== Overview ==
 
Link-based applications like [[Wikipedia]] are becoming increasingly popular because they provide users with an efficient way to find needed knowledge, such as searching for definitions and information about a particular topic, and exploring articles on related topics. This work introduces a semantics-based navigation application called WNavi^s, to facilitate information-seeking activities in internal link-based websites in Wikipedia. WNavi^s is based on the theories and techniques of link mining, semantic [[relatedness]] analysis and text summarization. Authors goal is to develop an application that helps users find related articles for a seed query (topic) easily and then quickly check the content of articles to explore a new concept or topic in Wikipedia. Technically, authors construct a preliminary topic network by analyzing the internal links of Wikipedia and applying the normalized [[Google]] distance algorithm to quantify the strength of the semantic relationships between articles via key terms. Because not all the content of articles in Wikipedia is relevant to users' information needs, it is desirable to locate specific information for users and enable them to quickly explore and read topic-related articles. Accordingly, authors propose an SNA-based single and multiple-document summarization technique that can extract meaningful sentences from articles. Authors applied a number of intrinsic and extrinsic evaluation methods to demonstrate the efficacy of the summarization techniques in terms of precision, and recall. The results suggest that the proposed summarization technique is effective. Authors findings have implications for the design of a navigation tool that can help users explore related articles in Wikipedia quickly.
 
Link-based applications like [[Wikipedia]] are becoming increasingly popular because they provide users with an efficient way to find needed knowledge, such as searching for definitions and information about a particular topic, and exploring articles on related topics. This work introduces a semantics-based navigation application called WNavi^s, to facilitate information-seeking activities in internal link-based websites in Wikipedia. WNavi^s is based on the theories and techniques of link mining, semantic [[relatedness]] analysis and text summarization. Authors goal is to develop an application that helps users find related articles for a seed query (topic) easily and then quickly check the content of articles to explore a new concept or topic in Wikipedia. Technically, authors construct a preliminary topic network by analyzing the internal links of Wikipedia and applying the normalized [[Google]] distance algorithm to quantify the strength of the semantic relationships between articles via key terms. Because not all the content of articles in Wikipedia is relevant to users' information needs, it is desirable to locate specific information for users and enable them to quickly explore and read topic-related articles. Accordingly, authors propose an SNA-based single and multiple-document summarization technique that can extract meaningful sentences from articles. Authors applied a number of intrinsic and extrinsic evaluation methods to demonstrate the efficacy of the summarization techniques in terms of precision, and recall. The results suggest that the proposed summarization technique is effective. Authors findings have implications for the design of a navigation tool that can help users explore related articles in Wikipedia quickly.
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=== Wikipedia Quality ===
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Wu, I-Chin; Lin, Yi-Sheng. (2012). "[[Wnavis: Navigating Wikipedia Semantically with an Sna-Based Summarization Technique]]". Elsevier Science Publishers B. V.. DOI: 10.1016/j.dss.2012.04.002.
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=== English Wikipedia ===
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{{cite journal |last1=Wu |first1=I-Chin |last2=Lin |first2=Yi-Sheng |title=Wnavis: Navigating Wikipedia Semantically with an Sna-Based Summarization Technique |date=2012 |doi=10.1016/j.dss.2012.04.002 |url=https://wikipediaquality.com/wiki/Wnavis:_Navigating_Wikipedia_Semantically_with_an_Sna-Based_Summarization_Technique |journal=Elsevier Science Publishers B. V.}}
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=== HTML ===
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Wu, I-Chin; Lin, Yi-Sheng. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wnavis:_Navigating_Wikipedia_Semantically_with_an_Sna-Based_Summarization_Technique">Wnavis: Navigating Wikipedia Semantically with an Sna-Based Summarization Technique</a>&amp;quot;. Elsevier Science Publishers B. V.. DOI: 10.1016/j.dss.2012.04.002.
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Latest revision as of 20:42, 24 March 2020


Wnavis: Navigating Wikipedia Semantically with an Sna-Based Summarization Technique
Authors
I-Chin Wu
Yi-Sheng Lin
Publication date
2012
DOI
10.1016/j.dss.2012.04.002
Links
Original

Wnavis: Navigating Wikipedia Semantically with an Sna-Based Summarization Technique - scientific work related to Wikipedia quality published in 2012, written by I-Chin Wu and Yi-Sheng Lin.

Overview

Link-based applications like Wikipedia are becoming increasingly popular because they provide users with an efficient way to find needed knowledge, such as searching for definitions and information about a particular topic, and exploring articles on related topics. This work introduces a semantics-based navigation application called WNavi^s, to facilitate information-seeking activities in internal link-based websites in Wikipedia. WNavi^s is based on the theories and techniques of link mining, semantic relatedness analysis and text summarization. Authors goal is to develop an application that helps users find related articles for a seed query (topic) easily and then quickly check the content of articles to explore a new concept or topic in Wikipedia. Technically, authors construct a preliminary topic network by analyzing the internal links of Wikipedia and applying the normalized Google distance algorithm to quantify the strength of the semantic relationships between articles via key terms. Because not all the content of articles in Wikipedia is relevant to users' information needs, it is desirable to locate specific information for users and enable them to quickly explore and read topic-related articles. Accordingly, authors propose an SNA-based single and multiple-document summarization technique that can extract meaningful sentences from articles. Authors applied a number of intrinsic and extrinsic evaluation methods to demonstrate the efficacy of the summarization techniques in terms of precision, and recall. The results suggest that the proposed summarization technique is effective. Authors findings have implications for the design of a navigation tool that can help users explore related articles in Wikipedia quickly.

Embed

Wikipedia Quality

Wu, I-Chin; Lin, Yi-Sheng. (2012). "[[Wnavis: Navigating Wikipedia Semantically with an Sna-Based Summarization Technique]]". Elsevier Science Publishers B. V.. DOI: 10.1016/j.dss.2012.04.002.

English Wikipedia

{{cite journal |last1=Wu |first1=I-Chin |last2=Lin |first2=Yi-Sheng |title=Wnavis: Navigating Wikipedia Semantically with an Sna-Based Summarization Technique |date=2012 |doi=10.1016/j.dss.2012.04.002 |url=https://wikipediaquality.com/wiki/Wnavis:_Navigating_Wikipedia_Semantically_with_an_Sna-Based_Summarization_Technique |journal=Elsevier Science Publishers B. V.}}

HTML

Wu, I-Chin; Lin, Yi-Sheng. (2012). &quot;<a href="https://wikipediaquality.com/wiki/Wnavis:_Navigating_Wikipedia_Semantically_with_an_Sna-Based_Summarization_Technique">Wnavis: Navigating Wikipedia Semantically with an Sna-Based Summarization Technique</a>&quot;. Elsevier Science Publishers B. V.. DOI: 10.1016/j.dss.2012.04.002.