Topic-aware visual citation tracing via enhanced term weighting for efficient literature retrieval
Authors
Zhao, YoubingWei, Hui
Wu, Shaopeng
Parvinzamir, Farzad
Deng, Zhikun
Zhao, Xia
Ersotelos, Nikolaos
Dong, Feng
Clapworthy, Gordon J.
Liu, Enjie
Affiliation
University of BedfordshireIssue Date
2017-12-31
Metadata
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DATA 2016: Data Management Technologies and ApplicationsAbstract
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.Citation
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', International Conference on Data Management Technologies and Applications - Colmar, Springer Verlag.Publisher
Springer VerlagAdditional Links
https://link.springer.com/chapter/10.1007%2F978-3-319-62911-7_5Type
Conference papers, meetings and proceedingsLanguage
enISBN
9783319629100ae974a485f413a2113503eed53cd6c53
10.1007/978-3-319-62911-7_5