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.
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Acknowledgments
The research is supported by the FP7 Programme of the European Commission within projects Dr Inventor [FP7-ICT-611383] and CARRE [FP7-ICT-611140]. We would like to thank the European Commission for the funding and thank the project officers and reviewers for their indispensable support for both of the projects.
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Zhao, Y. et al. (2017). Topic-Aware Visual Citation Tracing via Enhanced Term Weighting for Efficient Literature Retrieval. In: Francalanci, C., Helfert, M. (eds) Data Management Technologies and Applications. DATA 2016. Communications in Computer and Information Science, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-62911-7_5
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DOI: https://doi.org/10.1007/978-3-319-62911-7_5
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