An innovative citation recommendation model for draft papers with varying degrees of information completeness
| Date | 03 September 2019 |
| DOI | https://doi.org/10.1108/DTA-12-2018-0105 |
| Pages | 562-576 |
| Published date | 03 September 2019 |
| Author | Yen-Liang Chen,Cheng-Hsiung Weng,Cheng-Kui Huang,Duo-Jia Shih |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information & knowledge management,Information & communications technology,Internet |
An innovative citation
recommendation model for draft
papers with varying degrees of
information completeness
Yen-Liang Chen
National Central University, Jhongli, Taiwan
Cheng-Hsiung Weng
Central Taiwan University of Science and Technology, Taichung, Taiwan
Cheng-Kui Huang
National Chung Cheng University, Minhsiung, Taiwan, and
Duo-Jia Shih
National Central University, Jhongli, Taiwan
Abstract
Purpose –As researchers are writing a draft paper with incomplete structure or text, one of burdensome
tasks is to deliberate about which references should be cited for one sentence or paragraph of this draft. In
view of the rapid increase in the number of research papers, researchers desire to figure out a better way to do
citation recommendations in developing their draft papers. The purpose of this paper is to propose citation
recommendation algorithms that enable the acquisition of relevant citations for research papers that are still
at the drafting stage. This study attempts to help researchers to select appropriatereferences among the vast
amount of available papers and make draft papers complete in reference citation.
Design/methodology/approach –This study adopts a model for recommending citations for incomplete
drafts. Four algorithms are proposed in this study. The first and second algorithms are unsupervised models,
applying term frequency-inverse document frequency and WordNet technologies, respectively. The third and
fourth algorithms are based on the second algorithm to integrate different weight adjustment strategies to
improve performance.
Findings –The proposed recommendation method adopts three techniques, including using WordNet to
transform vector and setting adjustment weights according to structural factors and the information
completeness degree, to generate citation recommendation for incomplete drafts. The experiments show that
all these three techniques can significantly improve the recommendation accuracy.
Originality/value –None of the methods employed in previous studies can recommend articles as
references for incomplete drafts. This paper addresses the situation that a draft paper can be incomplete
either in structure or text or both. Recommended references, however, can be still generated and inserted into
any desired sentence of the draft paper.
Keywords Incompleteness, Text mining, Citation, Recommendation system, Draft paper, WordNet
Paper type Research paper
1. Introduction
Human culture has emerged through the accumulation and inheritance of knowledge.
Academic works have been based on the vast amount of knowledge that has been built over
many millennia. Since humans first began to research, numerous academic articles have
been written. In the information boom era, the number of such articles has grown even more
rapidly, and researchers are faced with an unprecedented increase in human knowledge.
This large volume of articles has made it difficult for researchers to write research papers,
particularly because of the difficulty in finding the most relevant articles. That is to say, this
issue is often confronted especially in the writing of draft papers. As writing a draft paper
(defined it as the content consisting of structure, paragraph, and sentence is incomplete),
Data Technologies and
Applications
Vol. 53 No. 4, 2019
pp. 562-576
© Emerald PublishingLimited
2514-9288
DOI 10.1108/DTA-12-2018-0105
Received 13 December 2018
Revised 16 May 2019
Accepted 11 September 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2514-9288.htm
562
DTA
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