Clustering and summarization topics of subject knowledge through analyzing internal links of Wikipedia

This work introduces a semantics-based navigation application called WNavi^s. It facilitates informationseeking activities in internal link-based websites within Wikipedia. Our 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. We 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, we propose a social network analysis (SNA)-based topic summarization technique that extracts meaningful sentences from articles. We applied a number of intrinsic evaluation methods to demonstrate the efficacy of the summarization techniques. Our findings have implications for the design of a navigation tool that can help users explore topics and increase their subject knowledge.