Mining and Visualization of Knowledge Evolution From An Academic Archive
Date Issued
2010
Date
2010
Author(s)
Lee, Ting-Yen
Abstract
The World Wide Web is a rich source of knowledge. As many documents on the web are unstructured, it is difficult for people to learn efficiently in a systematic fashion. To facilitate learning, we aim to organize web resources according to the evolution of topics, which is a common organization used in many textbooks. In this research, we propose an approach to mining knowledge evolution from an academic archive by taking advantage of the citations between the literatures. Relationships among keywords are first extracted from each paper, and a citation graph is constructed from the references among papers to capture their dependency over time. Similar keywords can then be clustered into aggregated topics to track the evolution of knowledge. Finally, we propose methods to visualize knowledge evaluation by viewing both the evolution and interaction of aggregated topics. The experiments are conducted on 32,044 papers crawled from the ACM Digital Library, and the results show that the proposed approach is effective in mining knowledge evolution to help users learn from online resources.
Subjects
Mining
Visualization
Knowledge Evolution
Academic Archive
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