近二十年引文推荐的系统回顾

IF 4.1 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal on Semantic Web and Information Systems Pub Date : 2023-06-01 DOI:10.4018/ijswis.324071
Yicong Liang, Lap-Kei Lee
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引用次数: 0

摘要

引文是对文章中使用的信息来源的参考。引文对学生和研究人员定位一个主题的相关信息非常有用。恰当的引文在文章写作的学术伦理中也很重要。由于每年发表的科学著作数量快速增长,如何向学生和研究人员自动推荐引文已成为一个有趣但具有挑战性的研究问题。特别是,引文推荐系统可以帮助学生识别相关的论文和文献进行学术写作。根据是否给出了特定的局部引文上下文,可以将引文推荐分为局部引文推荐和全局引文推荐;例如,引文占位符周围的文本。本文对全球引文推荐模型进行了系统的综述,并对传统的基于主题的模型和近年来嵌入深度神经网络的模型进行了比较,旨在总结这一领域的研究成果,为引文推荐的研究提供参考。
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A Systematic Review of Citation Recommendation Over the Past Two Decades
A citation is a reference to the source of information used in an article. Citations are very useful for students and researchers to locate relevant information on a topic. Proper citation is also important in the academic ethics of article writing. Due to the rapid growth of scientific works published each year, how to automatically recommend citations to students and researchers has become an interesting but challenging research problem. In particular, a citation recommendation system can assist students to identify relevant papers and literature for academic writing. Citation recommendation can be classified into local and global citation recommendation depending on whether a specific local citation context is given; e.g., the text surrounding a citation placeholder. This article provides a systematic review on global citation recommendation models and compares the reviewed methods from the traditional topic- based models to the recent models embedded with deep neural networks, aiming to summarize this field to facilitate researchers working on citation recommendation.
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来源期刊
CiteScore
6.20
自引率
12.50%
发文量
51
审稿时长
20 months
期刊介绍: The International Journal on Semantic Web and Information Systems (IJSWIS) promotes a knowledge transfer channel where academics, practitioners, and researchers can discuss, analyze, criticize, synthesize, communicate, elaborate, and simplify the more-than-promising technology of the semantic Web in the context of information systems. The journal aims to establish value-adding knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government.
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