WNavi ^s: Navigating Wikipedia semantically with an SNA-based summarization technique

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. Our 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, we 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, we propose an SNA-based single and multiple-document summarization technique that can extract meaningful sentences from articles. We 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. Our findings have implications for the design of a navigation tool that can help users explore related articles in Wikipedia quickly.