We search a variety of things over the Internet in our daily lives, and numerous search engines are available to get us more relevant results. With the rapid technological advancement, the internet has become a major ...We search a variety of things over the Internet in our daily lives, and numerous search engines are available to get us more relevant results. With the rapid technological advancement, the internet has become a major source of obtaining information. Further, the advent of the Web2.0 era has led to an increased interaction between the user and the website. It has become challenging to provide information to users as per their interests. Because of copyright restrictions, most of existing research studies are confronting the lack of availability of the content of candidates recommending articles. The content of such articles is not always available freely and hence leads to inadequate recommendation results. Moreover, various research studies base recommendation on user profiles. Therefore, their recommendation needs a significant number of registered users in the system. In recent years, research work proves that Knowledge graphs have yielded better in generating quality recommendation results and alleviating sparsity and cold start issues. Network embedding techniques try to learn high quality feature vectors automatically from network structures, enabling vector-based measurers of node relatedness. Keeping the strength of Network embedding techniques, the proposed citation-based recommendation approach makes use of heterogeneous network embedding in generating recommendation results. The novelty of this paper is in exploiting the performance of a network embedding approach i.e., matapath2vec to generate paper recommendations. Unlike existing approaches, the proposed method has the capability of learning low-dimensional latent representation of nodes (i.e., research papers) in a network. We apply metapath2vec on a knowledge network built by the ACL Anthology Network (all about NLP) and use the node relatedness to generate item (research article) recommendations.展开更多
The research trend in rare earths has been studied using the Chemical Abstracts (CA) data. The number of papers published from China has been increasing very rapidly since 2001 and today China is the top country in te...The research trend in rare earths has been studied using the Chemical Abstracts (CA) data. The number of papers published from China has been increasing very rapidly since 2001 and today China is the top country in terms of paper contribution on rare earths. This article presents a comparative study of R&D trends among China, Japan and USA.展开更多
近年来,科研论文的合著现象及其与论文影响力之间的关系受到广泛关注。本文以1997—2013年Financial Times TOP 45商学院国际期刊论文为研究对象,对作者数量与论文被引(高被引还是零被引)之间的关系进行实证研究,从论文影响力方面揭示...近年来,科研论文的合著现象及其与论文影响力之间的关系受到广泛关注。本文以1997—2013年Financial Times TOP 45商学院国际期刊论文为研究对象,对作者数量与论文被引(高被引还是零被引)之间的关系进行实证研究,从论文影响力方面揭示商学领域是否存在最佳科研合作规模。研究发现:①与单独作者相比,多作者合作对论文总被引频次具有显著的正向影响,而且多作者合作论文成为高被引的概率更高,而成为零被引的概率更低;②作者数量与论文总被引频次之间存在显著的倒U形关系;进一步研究发现,作者数量与高被引论文概率呈倒U形关系,而与零被引论文概率呈正U形关系,且转折点均约为3人,表明商学领域存在使论文成为高被引而避免成为零被引的最佳合作规模;③分时间阶段实证结果表明,基于高被引和零被引的论文最佳合作规模逐步由2~3人增加至3~4人。展开更多
[目的/意义]以我国图情领域为例,测量论文的新颖性和传统性并探究其对论文学术影响力的作用进而揭示学术创新的规律.[方法/过程]采用基于马尔科夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)的方法,对我国2000年至2019年20年间在中文社...[目的/意义]以我国图情领域为例,测量论文的新颖性和传统性并探究其对论文学术影响力的作用进而揭示学术创新的规律.[方法/过程]采用基于马尔科夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)的方法,对我国2000年至2019年20年间在中文社会科学引文索引(CSSCI)中收录的图书馆学情报学领域的70207篇研究论文的新颖性、传统性进行测量,并分析论文新颖性和传统性对论文学科影响力的作用.[结果/结论]结果显示,其他因素不变时,论文新颖性提高1个单位,论文成为高被引论文的优势比增加11%,而论文传统性提高1个单位,论文成为高被引论文的优势比增加33%.边际效应分析显示,同时具有较高的新颖性和传统性的论文较之于其他类型的论文具有更高的成为高被引论文的可能性.此外,随着时间推移,新颖性对论文成为高被引论文概率的影响逐渐削弱,而传统性的影响逐渐增强.同时,作者团队规模对于论文的新颖性存在显著影响,这种影响随着时间的推移而增强.这些发现凸显我国图情领域守正创新的特点,为理解我国图情领域的学术创新规律提供新的实证基础.同时,也提出一种不同于传统信息计量的基于贝叶斯统计的新方法.展开更多
文摘We search a variety of things over the Internet in our daily lives, and numerous search engines are available to get us more relevant results. With the rapid technological advancement, the internet has become a major source of obtaining information. Further, the advent of the Web2.0 era has led to an increased interaction between the user and the website. It has become challenging to provide information to users as per their interests. Because of copyright restrictions, most of existing research studies are confronting the lack of availability of the content of candidates recommending articles. The content of such articles is not always available freely and hence leads to inadequate recommendation results. Moreover, various research studies base recommendation on user profiles. Therefore, their recommendation needs a significant number of registered users in the system. In recent years, research work proves that Knowledge graphs have yielded better in generating quality recommendation results and alleviating sparsity and cold start issues. Network embedding techniques try to learn high quality feature vectors automatically from network structures, enabling vector-based measurers of node relatedness. Keeping the strength of Network embedding techniques, the proposed citation-based recommendation approach makes use of heterogeneous network embedding in generating recommendation results. The novelty of this paper is in exploiting the performance of a network embedding approach i.e., matapath2vec to generate paper recommendations. Unlike existing approaches, the proposed method has the capability of learning low-dimensional latent representation of nodes (i.e., research papers) in a network. We apply metapath2vec on a knowledge network built by the ACL Anthology Network (all about NLP) and use the node relatedness to generate item (research article) recommendations.
文摘The research trend in rare earths has been studied using the Chemical Abstracts (CA) data. The number of papers published from China has been increasing very rapidly since 2001 and today China is the top country in terms of paper contribution on rare earths. This article presents a comparative study of R&D trends among China, Japan and USA.
文摘近年来,科研论文的合著现象及其与论文影响力之间的关系受到广泛关注。本文以1997—2013年Financial Times TOP 45商学院国际期刊论文为研究对象,对作者数量与论文被引(高被引还是零被引)之间的关系进行实证研究,从论文影响力方面揭示商学领域是否存在最佳科研合作规模。研究发现:①与单独作者相比,多作者合作对论文总被引频次具有显著的正向影响,而且多作者合作论文成为高被引的概率更高,而成为零被引的概率更低;②作者数量与论文总被引频次之间存在显著的倒U形关系;进一步研究发现,作者数量与高被引论文概率呈倒U形关系,而与零被引论文概率呈正U形关系,且转折点均约为3人,表明商学领域存在使论文成为高被引而避免成为零被引的最佳合作规模;③分时间阶段实证结果表明,基于高被引和零被引的论文最佳合作规模逐步由2~3人增加至3~4人。
文摘随着Web 2.0和社交网络的发展,补充学术成果评价的Altmetrics指标应运而生,已有研究表明Altmetrics指标与被引频次之间存在相关性,但集成Altmetrics指标的论文高被引预测研究较少。因此,基于引用理论,将Altemetrics指标与学术层面指标相结合,构建论文高被引预测的指标体系;选取ESI高被引论文榜单,获取2022年4月经济与商业学科高被引论文合集,由此从Web of Science数据库获取论文集相关的学术层面数据,并从Altmetric LLP平台获取论文集相关的Altmetrics指标数据;经过数据清洗和预处理,共得到27953篇论文数据,对比3种常用机器学习算法的论文高被引预测结果,得到最优的预测模型。研究结果表明:相较于仅使用学术层面指标,引入Altmetrics指标的论文高被引预测效果更优;Altmetrics指标中的在线阅读平台读者数对论文被引频次的影响最大,随后是学术层面指标中的期刊被引半衰期、论文首次被引两年内被引频次、一作总被引频次。研究可以为探究论文高被引的影响因素及其影响程度,完善学术成果的评价体系提供理论依据。
文摘[目的/意义]以我国图情领域为例,测量论文的新颖性和传统性并探究其对论文学术影响力的作用进而揭示学术创新的规律.[方法/过程]采用基于马尔科夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)的方法,对我国2000年至2019年20年间在中文社会科学引文索引(CSSCI)中收录的图书馆学情报学领域的70207篇研究论文的新颖性、传统性进行测量,并分析论文新颖性和传统性对论文学科影响力的作用.[结果/结论]结果显示,其他因素不变时,论文新颖性提高1个单位,论文成为高被引论文的优势比增加11%,而论文传统性提高1个单位,论文成为高被引论文的优势比增加33%.边际效应分析显示,同时具有较高的新颖性和传统性的论文较之于其他类型的论文具有更高的成为高被引论文的可能性.此外,随着时间推移,新颖性对论文成为高被引论文概率的影响逐渐削弱,而传统性的影响逐渐增强.同时,作者团队规模对于论文的新颖性存在显著影响,这种影响随着时间的推移而增强.这些发现凸显我国图情领域守正创新的特点,为理解我国图情领域的学术创新规律提供新的实证基础.同时,也提出一种不同于传统信息计量的基于贝叶斯统计的新方法.
