Difference between revisions of "Clustering and Summarization Topics of Subject Knowledge Through Analyzing Internal Links of Wikipedia"
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| + | | title = Clustering and Summarization Topics of Subject Knowledge Through Analyzing Internal Links of Wikipedia | ||
| + | | date = 2013 | ||
| + | | authors = [[I-Chin Wu]]<br />[[Chi-Hong Tsai]]<br />[[Yu-Hsuan Lin]] | ||
| + | | doi = 10.1109/IRI.2013.6642458 | ||
| + | | link = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6642458 | ||
| + | }} | ||
'''Clustering and Summarization Topics of Subject Knowledge Through Analyzing Internal Links of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[I-Chin Wu]], [[Chi-Hong Tsai]] and [[Yu-Hsuan Lin]]. | '''Clustering and Summarization Topics of Subject Knowledge Through Analyzing Internal Links of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[I-Chin Wu]], [[Chi-Hong Tsai]] and [[Yu-Hsuan Lin]]. | ||
== Overview == | == Overview == | ||
This work introduces a semantics-based navigation application called WNavi s . It facilitates informationseeking activities in internal link-based websites within [[Wikipedia]]. Authors goal is to develop an application that helps users easily find related articles on a given topic and then quickly check the content of articles to explore concepts in Wikipedia. Authors constructed a subject-based network by analyzing the internal links of Wikipedia and applying a semantic [[relatedness]] analysis to measure the strength of the semantic relationships between articles. In order to locate specific information and enable users to quickly explore and read subject-related articles, authors propose a [[social network]] analysis (SNA)-based topic summarization technique that extracts meaningful sentences from articles. Authors applied a number of intrinsic evaluation methods to demonstrate the efficacy of the summarization techniques. Authors findings have implications for the design of a navigation tool that can help users explore topics and increase their subject knowledge. | This work introduces a semantics-based navigation application called WNavi s . It facilitates informationseeking activities in internal link-based websites within [[Wikipedia]]. Authors goal is to develop an application that helps users easily find related articles on a given topic and then quickly check the content of articles to explore concepts in Wikipedia. Authors constructed a subject-based network by analyzing the internal links of Wikipedia and applying a semantic [[relatedness]] analysis to measure the strength of the semantic relationships between articles. In order to locate specific information and enable users to quickly explore and read subject-related articles, authors propose a [[social network]] analysis (SNA)-based topic summarization technique that extracts meaningful sentences from articles. Authors applied a number of intrinsic evaluation methods to demonstrate the efficacy of the summarization techniques. Authors findings have implications for the design of a navigation tool that can help users explore topics and increase their subject knowledge. | ||
Revision as of 09:01, 15 April 2020
Authors | I-Chin Wu Chi-Hong Tsai Yu-Hsuan Lin |
|---|---|
Publication date | 2013 |
DOI | 10.1109/IRI.2013.6642458 |
Links | Original |
Clustering and Summarization Topics of Subject Knowledge Through Analyzing Internal Links of Wikipedia - scientific work related to Wikipedia quality published in 2013, written by I-Chin Wu, Chi-Hong Tsai and Yu-Hsuan Lin.
Overview
This work introduces a semantics-based navigation application called WNavi s . It facilitates informationseeking activities in internal link-based websites within Wikipedia. Authors goal is to develop an application that helps users easily find related articles on a given topic and then quickly check the content of articles to explore concepts in Wikipedia. Authors constructed a subject-based network by analyzing the internal links of Wikipedia and applying a semantic relatedness analysis to measure the strength of the semantic relationships between articles. In order to locate specific information and enable users to quickly explore and read subject-related articles, authors propose a social network analysis (SNA)-based topic summarization technique that extracts meaningful sentences from articles. Authors applied a number of intrinsic evaluation methods to demonstrate the efficacy of the summarization techniques. Authors findings have implications for the design of a navigation tool that can help users explore topics and increase their subject knowledge.
