Difference between revisions of "Clustering and Summarization Topics of Subject Knowledge Through Analyzing Internal Links of Wikipedia"
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− | '''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 09:12, 9 October 2019
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.