The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Placing search in context: the concept revisited
Proceedings of the 10th international conference on World Wide Web
Identifying topics by position
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Journal of Information Science
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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.