共查询到19条相似文献，搜索用时 187 毫秒
1.
2.
3.
4.
作者同被引分析(ACA)方法是研究领域知识结构的重要手段,它能够发现跨领域知识关联的存在,借助一些辅助的分析方法和步骤也能够发现知识关联的内容.但作者同被引网络无法直接反映关联的内容,因此这种方法的实际应用效果受到一定限制.施引关键词与被引作者的交叉共现分析(CKCA),能够清楚地发现科学领域中的学术流派及研究方向,以及流派中作者间关联的内容,发现在某一主题领域有重要贡献的研究人员.与作者同被引分析(ACA)相比,CKCA分析在揭示学术流派间、学术流派内节点间联系的能力更加出色,能够更准确的反应学术流派的研究主题和方向,并且具备了更高的可读性. 相似文献
5.
6.
作者学术关系研究进展 总被引：1，自引：0，他引：1
[目的/意义] 作者学术关系是指从知识交流的角度出发,作者与作者之间表现出来的某种学术联系。对作者学术关系的研究有利于发现作者之间研究的相关度、学术共同体和科学知识结构,促进知识交流和知识传播,对知识创新和国家知识体系的健全和发展具有重要意义。[方法/过程] 采用综合归纳的方法,从作者合作关系、作者引用关系和作者链接关系3个方面对国内外作者学术关系的研究现状进行梳理。[结果/结论] 国内外学者对作者合作关系、作者共被引关系及作者链接关系的研究趋于丰富,对作者互引关系、作者耦合关系的研究还有待提升;研究领域主要集中在图书情报学领域。未来对作者学术关系的研究方向为作者重名问题分析、大数据引文挖掘及作者学术关系的拓展研究等。 相似文献
7.
基于Web of Science和中国知网中“链接分析”方面的文献数据,首先简要介绍了国内外链接分析研究的发展趋势,然后融合Citespace Ⅱ、SATI 3.1和Netdraw等文献计量与可视化分析软件构建了:①引文共被引聚类图谱,揭示出国际链接分析研究的起源文献和核心知识基础.②国内外Top100作者形成的科研合作网络,揭示出国内外链接分析领域科研团队的数量和规模情况,以及这些科研团队形成的合作模式.③引用期刊共被引聚类网络,揭示出国际链接分析研究的主要参考期刊.④主要发文国家的科研合作网络,揭示出国际链接分析领域发文量最大的国家是美国,其次是中国,发文量较大国家之间的科研合作较少.⑤国内外全时段链接分析文献关键词的共现知识图谱,识别出国内外链接分析领域的研究热点.⑥国内外分时段链接分析文献关键词的共现知识图谱,通过对比分析不同时段的关键词数据,识别出国内外链接分析研究近5年(2009～ 2013年)的前沿热点主题. 相似文献
8.
[目的/意义] 当前知识聚合模式研究侧重"依据何种知识关联开展知识聚合",本文补充性地探索"利用知识关联将知识单元聚合成何种形式"这一后续问题,以完善知识聚合模式研究和引导实践的深入。[方法/过程] 借鉴化学领域中对聚合反应两大类型划分的方法,提出以"是否保留知识单元间及其关联间的差异性"为根据,将知识聚合划分为知识类聚和知识共聚,并探讨知识共聚的基本实现形式。[结果/结论] 领域知识是知识共聚开展的基础;以文档、词语为基本知识单元粒度,以用户需求入口和聚合目标资源为维度,知识共聚可通过四种基本形式实现：基于分面导航、基于多维概念关联推荐、基于知识元链接、基于资源潜在关联发现。 相似文献
9.
本文研究了知识网络中的文献共被引网络,它形成着一个学科的知识基础,是学科知识输入和转移的重要知识之源。文章在总结和梳理了共被引网络的一般研究方法后,采集了1900~2012年图书馆与情报学的71350条题录数据,构建了期刊共被引知识网络与作者共被引等知识网络,并对其进行深入数据挖掘和分析,发现了经常反复被引的学科内核心期刊与作者群体,以及在学科的演化历史上连接前后时段的关键性节点期刊与作者群体。论文的最后还对文献共被引网络方法局限性方面和未来发展方面进行了讨论与总结,指出在解决现有理论难题后,共被引网络未来在各具体学科领域内的深化将是其发挥用武之地的重要发展方向。 相似文献
10.
知识图谱是利用计算机存储、管理和呈现概念及其相互关系的一种技术,一经提出便很快成为工业界和学术界的研究热点,但目前对知识图谱的认知还比较混乱。依据存储方式不同,知识图谱可分为基于RDF存储的语义知识图谱（关联数据）和基于图数据库的广义知识图谱。语义知识图谱（关联数据）侧重于知识的发布和链接,广义知识图谱则更侧重于知识的挖掘和计算,两者之间既有共同点,又有不同之处。本文从概念层面和技术层面详细分析了两者之间的异同,指出语义知识图谱（关联数据）才是谷歌知识图谱的延续和发展。随后,提出了将知识图谱应用于数字人文研究的系统框架,并在此基础上构建了中国历代人物传记资料库的关联数据平台（CBDBLD）。该平台借助知识图谱的理念展现了人物之间丰富的亲属及社会关系,形成了特有的社会关系网络,并可通过设置推理规则来实现人物之间隐性关系的挖掘与呈现。广义知识图谱研究中丰富的图运算和关联数据的结合将会成为数字人文领域研究的下一个热点,从而开启数字人文研究的新时代。图10。表2。参考文献25。 相似文献
11.
《Journal of Informetrics》2023,17(1):101374
Academic collaboration prediction is considered to be an important way to help scholars expand their research horizons and explore a vast and suitable range of partners. However, existing studies mainly rely on historical collaborations for future predictions, which has limitations in digging into credible collaboration possibilities in a wide range of cross-disciplinary contexts. In view of this, this study tries to combine three typical citation relationships (including direct citation, co-citation, and coupling) to predict prospective collaborations based on citation information that reflects the characteristics of scholars’ knowledge structure and research habits, which is supposed to provide supplement and extension for traditional implementation. To this end, we construct all-author tripartite citation networks based on the bibliographic data in the field of gene editing, and apply the Node2vec and Multi-node2vec algorithms to predict collaborations between authors in both single and multiple layers. According to compare with that of link prediction indicators (including CN, AA, PA and RA, etc.) commonly used for traditional collaboration networks, it is found that the prediction results in the multilayer all-author tripartite citation network should be relatively more accurate. The results will be helpful for scholars in the field of gene editing to explore potential collaborators with an implicit research connection. 相似文献
12.
《Journal of Informetrics》2020,14(4):101079
The rapid development of scientific fields in this modern era has raised the concern for prospective scholars to find a proper research field to conduct their future studies. Thus, having a vision of future could be helpful to pick the right path for doing research and ensuring that it is worth investing in. In this study, we use article keywords of computer science journals and conferences, assigned by INSPEC controlled indexing, to construct a temporal scientific knowledge network. By observing keyword networks snapshots over time, we can utilize the link prediction methods to foresee the future structures of these networks. We use two different approaches for this link prediction problem. First, we have utilized three topology-based link prediction algorithms, two of which are commonly used in literature. We have also proposed a third algorithm based on nodes (keywords) clustering coefficient, their centrality measures like eigenvector centrality, and nodes community information. Then, we used nodes topological features and the outputs of aforementioned topology-based link prediction algorithms as features to feed five machine learning link prediction algorithms (SVM, Random Forest Classifier, K-Nearest Neighbors, Gaussian Naïve Bayes, and Multinomial Naïve Bayes). All tested predictors have shown considerable performance and their results are discussed in this paper. 相似文献
13.
当前,针对知识网络的链路预测主要是基于网络拓扑结构的相似性,很少考虑作者的研究领域,导致信息利用不充分等问题,因此本文提出了双层知识网络的链路预测框架hypernet2vec。双层知识网络,即作者合著关系网络和学术领域关系网络,利用网络表示学习,分别将两层网络中的节点映射到低维的向量空间,再输入到专门设计的卷积神经网络中计算并进行链路预测。与经典的链路预测指标如RA指标、LP指标和LRW指标等相比,hypernet2vec模型预测的AUC(area under curve)值取得了显著的提升,平均提升幅度达11.17%。文章还从情报产生层面和复杂系统层面,对模型发生作用的深层机理进行了探讨。 相似文献
14.
Jovan Pehcevski James A. Thom Anne-Marie Vercoustre Vladimir Naumovski 《Information Retrieval》2010,13(5):568-600
Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity
extraction where the goal is to tag names of entities in documents, entity ranking is primarily focused on returning a ranked
list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated
on the INEX Wikipedia test collection. In this paper, we describe a system we developed for ranking Wikipedia entities in
answer to a query. The entity ranking approach implemented in our system utilises the known categories, the link structure
of Wikipedia, as well as the link co-occurrences with the entity examples (when provided) to retrieve relevant entities as
answers to the query. We also extend our entity ranking approach by utilising the knowledge of predicted classes of topic
difficulty. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition
to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the
optimal values for the retrieval parameters of our entity ranking system. Our experiments demonstrate that the use of categories
and the link structure of Wikipedia can significantly improve entity ranking effectiveness, and that topic difficulty prediction
is a promising approach that could also be exploited to further improve the entity ranking performance. 相似文献
15.
[目的/意义]学科交叉融合使得学科间知识交流日益频繁,从个体引文网络和整体引文网络入手,对我国人文社会科学领域跨学科知识流动进行量化分析,对“新文科”背景下该领域学科的守正与创新具有重要意义。[方法/过程]以2016-2020年23个学科450本期刊的论文引用关系为数据源,基于个体引文网络,从23个学科自身出发,根据学科互引关系确定模糊规则,利用Matlab进行模糊推理,确定学科知识固化程度;基于整体引文网络,运用“累积”的思想,计算学科知识累积流动率和累积影响力,根据知识流动情况划分学科类型。[结果/结论]研究结果表明,从个体引文网络视角分析,语言学、体育学、法学综合知识固化程度较高,统计学综合知识固化程度最低;从整体引文网络视角分析,将该领域23个学科根据知识流动划分为3种类型,经济学和管理学的累积影响力最大。研究发现“累积”思想对学科的评价效力优于直接引文分析,能够挖掘“隐藏”的学科知识流动潜在信息,为我国人文社科领域的学科建设和发展提供一定的启示。 相似文献
16.
《Journal of Informetrics》2022,16(2):101265
Linked topics in science and technology (LTSTs) can provide new avenues for technological innovation and are a key step in the transition from basic to applied research. This paper proposes a science and technology semantic linkage integration model for discovering LTSTs. Particularly, the integrative model fuses the term co-occurrence networks of basic and applied research, which expands the completeness of topic networks by enhancing the semantic characteristics of these networks. It is found that link prediction can further reinforce the semantic association of topic terms in networks between basic and applied topics. Simple fusion explicitly linked the topic terms, which can be used as automatic seed marking for subsequent link prediction to identify implicit linking of topic terms. Furthermore, an application to the gene-engineered vaccines field depicted that newly predicted implicit relations can effectively identify LTSTs. The results also show that implicit semantic recognition of LTSTs can be enhanced through simple fusion, while the recognition of LTST can be improved through link prediction. Therefore, the proposed model can assist experts to identify LTSTs that cannot be recognized through simple fusion. 相似文献
17.
As technological convergence has recently become a mainstream innovation trend, technological opportunities need to be explored in heterogeneous technology fields. Most of the previous convergence studies have taken a retrospective view in measuring the degree of convergence and monitoring the converging trends. This paper proposes a quantitative future-oriented approach to technological opportunity discovery for convergence using patent information. In a future-oriented approach, technological opportunities for convergence are suggested by predicting potential technological knowledge flows (TKFs) between heterogeneous fields. The potential TKFs are predicted by a link prediction method in a directed network, which is suggested in this paper to represent the direction of the predicted TKFs by adapting the concept of bibliographic coupling and edge-betweenness centrality. Converging technological opportunities are proposed as incremental and radical technological opportunities by extracting the potential increased knowledge flow links and emerging knowledge flow links. Moreover, the direction and themes of the predicted potential TKFs are provided as technological opportunities for convergence. As an illustration of the proposed method, the technological opportunities between biotechnology (BT) and information technology (IT) are explored. Firms and researchers can use the proposed method to seek out new technological opportunities from various technologies so that R&D policymakers can plan new R&D projects on technological convergence. 相似文献
18.
[目的/意义]分析和研究环境/生态学科的现状及国际学术合作情况,旨在了解我国在该领域范围内的优势及不足,为我国未来生态环境领域的科研活动以及国家合作方向提供借鉴。[方法/过程]本文以2009—2019年WOS核心数据库中的5640篇环境/生态学科高被引论文为数据源,对时空分布与影响力进行计量分析,同时运用复杂网络分析法,构建国际合作网络结构,探析合作的现状和特点。[结果/结论]结果显示:环境/生态学高被引论文的国际合作研究呈现积极上升态势,各国间知识流动日益频繁,但国家间合作分布异质性明显。中国在该学科高被引论文发表数以绝对优势占居领先地位,但论文国际合作比例偏低,未来需要加强论文的原始创新,提高研究成果的国际影响力。 相似文献
19.
[目的/意义]在引文分析中,可通过论文的一些属性特征对其未来的被引情况进行预测,并通过预测结果对论文、论文作者、作者所属机构及出版物做出评价。[方法/过程] 从出版物、作者和论文三个方面对影响论文被引的多个因素展开研究,以图书馆学情报学领域被SCI索引的论文作为分析及验证数据,使用逻辑回归、GBDT、XGBoost、AdaBoost、随机森林等算法进行预测,使用多组评测指标对比不同预测方法的效果,并使用GBDT识别对论文被引影响较大的因素。[结果/结论]确定三个方面的影响因素对论文被引预测的影响程度,构建预测模型,并较好地预测论文在未来一段时间的被引情况。大量实验分析发现GBDT、XGBoost和随机森林的预测能力较强,且预测的时间段越长,效果也就相对越好。 相似文献