An exploratory study of navigating Wikipedia semantically: Model and application

Abstract

Due to the popularity of link-based applications like Wikipedia, one of the most important issues in online research is how to alleviate information overload on the World Wide Web (WWW) and facilitate effective information-seeking. To address the problem, we propose a semantically-based navigation application that is based on the theories and techniques of link mining, semantic relatedness analysis and text summarization. Our goal is to develop an application that assists users in efficiently finding the related subtopics for a seed query and then quickly checking the content of articles. We establish a topic network by analyzing the internal links of Wikipedia and applying the Normalized Google Distance algorithm in order to quantify the strength of the semantic relationships between articles via key terms. To help users explore and read topic-related articles, we propose a SNA-based summarization approach to summarize articles. To visualize the topic network more efficiently, we develop a semantically-based WikiMap to help users navigate Wikipedia effectively. © 2011 Springer-Verlag.