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Topic-aware visual citation tracing via enhanced term weighting for efficient literature retrieval

  • Youbing Zhao
    ,
  • Hui Wei
    ,
  • Shaopeng Wu
    ,
  • Farzad Parvinzamir
    ,
  • Zhikun Deng
    ,
  • Xia Zhao
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

Efficient retrieval of scientific literature related to a certain topic plays a key role in research work. While little has been done on topic-enabled citation filtering in traditional citation tracing, this paper presents visual citation tracing of scientific papers with document topics taken into consideration. Improved term selection and weighting are employed for mining the most relevant citations. A variation of the TF-IDF scheme, which uses external domain resources as references is proposed to calculate the term weighting in a particular domain. Moreover document weight is also incorporated in the calculation of term weight from a group of citations. A simple hierarchical word weighting method is also presented to handle keyword phrases. A visual interface is designed and implemented to interactively present the citation tracks in chord diagram and Sankey diagram.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 79-101

Publication milestones

  • Published - 31/12/2017

Publication status

Published - 31/12/2017

Volume

737

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

ISBN (Print)

9783319629100

Publication IDs

  • handle.net: 10547/624191
  • Scopus: 85025176944

Host publication title

DATA 2016: Data Management Technologies and Applications

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