Acquisition of Scientific Literatures based on Citation-reason Visualization

Dongli Han, Hiroshi Koide, Ayato Inoue

2016

Abstract

When carrying out scientific research, the first step is to acquire relevant papers. It is easy to grab vast numbers of papers by inputting a keyword into a digital library or an online search engine. However, reading all the retrieved papers to find the most relevant ones is agonizingly time-consuming. Previous works have tried to improve paper search by clustering papers with their mutual similarity based on reference relations, including limited use of the type of citation (e.g. providing background vs. using specific method or data). However, previously proposed methods only classify or organize the papers from one point of view, and hence not flexible enough for user or context-specific demands. Moreover, none of the previous works has built a practical system based on a paper database. In this paper, we first establish a paper database from an open-access paper source, then use machine learning to automatically predict the reason for each citation between papers, and finally visualize the resulting information in an application system to help users more efficiently find the papers relevant to their personal uses. User studies employing the system show the effectiveness of our approach.

Paper Citation

in Harvard Style

Han D., Koide H. and Inoue A. (2016). Acquisition of Scientific Literatures based on Citation-reason Visualization . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: IVAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 123-130. DOI: 10.5220/0005693801230130

in Bibtex Style

@conference{ivapp16,
author={Dongli Han and Hiroshi Koide and Ayato Inoue},
title={Acquisition of Scientific Literatures based on Citation-reason Visualization},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: IVAPP, (VISIGRAPP 2016)},
year={2016},
pages={123-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005693801230130},
isbn={978-989-758-175-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: IVAPP, (VISIGRAPP 2016)
TI - Acquisition of Scientific Literatures based on Citation-reason Visualization
SN - 978-989-758-175-5
AU - Han D.
AU - Koide H.
AU - Inoue A.
PY - 2016
SP - 123
EP - 130
DO - 10.5220/0005693801230130