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, Nikolaos Ersotelos, Feng Dong, Gordon Clapworthy, Enjie Liu

Research output: Chapter in Book/Report/Conference proceedingChapter


© Springer International Publishing AG 2017. 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.
Original languageEnglish
Title of host publicationCommunications in Computer and Information Science
Number of pages23
ISBN (Print)9783319629100
Publication statusPublished - 2017
Externally publishedYes

Publication series

NameCommunications in Computer and Information Science


  • Citation tracing
  • Data management
  • Ontology
  • TF-IDF
  • Term weighting
  • Text mining
  • Visualization

Cite this

Zhao, Y., Wei, H., Wu, S., Parvinzamir, F., Deng, Z., Zhao, X., Ersotelos, N., Dong, F., Clapworthy, G., & Liu, E. (2017). Topic-aware visual citation tracing via enhanced term weighting for efficient literature retrieval. In Communications in Computer and Information Science (pp. 79-101). (Communications in Computer and Information Science; Vol. 737). Springer-Verlag.