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

doi:10.1007/978-3-319-62911-7_5

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

University of Bedfordshire

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	DATA 2016: Data Management Technologies and Applications
Publisher	Springer
Pages	79-101
Volume	737
ISBN (Print)	9783319629100
DOIs	https://doi.org/10.1007/978-3-319-62911-7_5
Publication status	Published - 31 Dec 2017
Event	International Conference on Data Management Technologies and Applications - Colmar Duration: 24 Jul 2016 → 26 Jul 2016

Conference

Conference	International Conference on Data Management Technologies and Applications
City	Colmar
Period	24/07/16 → 26/07/16
Other	International Conference on Data Management Technologies and Applications (24/07/2016-26/07/2016, Colmar)

Keywords

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

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10.1007/978-3-319-62911-7_5

https://link.springer.com/chapter/10.1007%252F978-3-319-62911-7_5

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@inproceedings{3e97577fb3db4ea389aa5cf5b9f80d42,

title = "Topic-aware visual citation tracing via enhanced term weighting for efficient literature retrieval",

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.",

keywords = "Citation tracing, Data management, Ontology, TF-IDF, Term weighting, Visualization, text mining",

author = "Youbing Zhao and Hui Wei and Shaopeng Wu and Farzad Parvinzamir and Zhikun Deng and Xia Zhao and Nikolaos Ersotelos and Feng Dong and Gordon Clapworthy and Enjie Liu",

year = "2017",

month = dec,

day = "31",

doi = "10.1007/978-3-319-62911-7\_5",

language = "English",

isbn = "9783319629100",

volume = "737",

pages = "79--101",

booktitle = "DATA 2016: Data Management Technologies and Applications",

publisher = "Springer",

address = "Germany",

note = "International Conference on Data Management Technologies and Applications ; Conference date: 24-07-2016 Through 26-07-2016",

}

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 DATA 2016: Data Management Technologies and Applications. vol. 737, Springer, pp. 79-101, International Conference on Data Management Technologies and Applications, Colmar, 24/07/16. https://doi.org/10.1007/978-3-319-62911-7_5

TY - GEN

T1 - Topic-aware visual citation tracing via enhanced term weighting for efficient literature retrieval

AU - Zhao, Youbing

AU - Wei, Hui

AU - Wu, Shaopeng

AU - Parvinzamir, Farzad

AU - Deng, Zhikun

AU - Zhao, Xia

AU - Ersotelos, Nikolaos

AU - Dong, Feng

AU - Clapworthy, Gordon

AU - Liu, Enjie

PY - 2017/12/31

Y1 - 2017/12/31

N2 - 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.

AB - 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.

KW - Citation tracing

KW - Data management

KW - Ontology

KW - TF-IDF

KW - Term weighting

KW - Visualization

KW - text mining

U2 - 10.1007/978-3-319-62911-7_5

DO - 10.1007/978-3-319-62911-7_5

M3 - Conference contribution

SN - 9783319629100

VL - 737

SP - 79

EP - 101

BT - DATA 2016: Data Management Technologies and Applications

PB - Springer

T2 - International Conference on Data Management Technologies and Applications

Y2 - 24 July 2016 through 26 July 2016

ER -

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

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Keywords

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