域名分析法的研究Ⅱ——应用研究

网络信息计量学是由文献计量学衍生出来的,其研究方法表现为移植文献计量学的研究方法,如网络链接分析法、网络内容分析法。对域名分析法的系统研究,有利于形成网络信息计量学真正意义上的特征研究方法。域名分析法应该包含网络日志、网络流量、网络引文等主体分析内容。文中对域名分析法的基本原理做了阐述;在此基础上,从网络日志分析、网络流量分析、网络引文分析角度出发详细介绍域名分析法的具体应用。     

《中国微创外科杂志》2001~2010年高被引论文分析

应用中国知网(CNKI)学术文献总库中的中国引文数据库、中国学术期刊网络出版总库对《中国微创外科杂志》2001～2010年发表的81篇被引频次≥20的高被引论文进行统计分析,分析高被引论文的发表时间、栏目、作者、作者单位,为编辑策划、选题组稿、组织专题提供参考依据。     

2006~2010年《作物学报》载文被引情况分析

《作物学报》是作物科学领域的领衔期刊,分析其载文被引情况可以了解该刊近年来的变化和发展。利用中国知网“中国引文数据库”的引文检索功能,统计了该刊2006~2010年5年间的被引用情况,从被引论文篇数、篇均被引频次、被引论文年代分布、被引论文作者分布和引用期刊及分布等方面进行综合分析。以期为本刊和农学类期刊的编辑出版提供参考。     

基于P-W型网络引文引证可追溯性研究——以情报学期刊为例

网络引文引证的可追溯性研究一直是学术界普遍关注的热点.通过获取CSSCI 2010年收录的情报学期刊论文被科研人员引证的网络引文作为数据样本,实证分析情报学科研人员引证网络引文的总体可追溯情况,不同域名、网页类型网络引文与可追溯性以及URLs深度与可追溯性之间的关系,并最终针对可追溯性问题提出相应的策略来提高科研人员学术交流的效果.     

《情报科学》2006-2010年高被引论文分析

以中国知网《中国学术文献网络出版总库》为统计源,从文献引证的角度分析《情报科学》高被引论文的分布规律。研究结果表明:该数据库共收录《情报科学》2006-2010年原文2039篇,被引文献1377篇,被引率为67.53%,总被引频次为7581,单篇最高被引频次81,较少的论文拥有较高的被引频次,基本符合"二八定律"。并且以被引频次≥21的前50篇论文作为研究对象,对其年代分布、被引频次、下载频次等方面进行了统计分析。     

综合类科技期刊网络引文的计量研究

以4种综合类科技核心期刊2007-2009年的载文为样本,对其网络引文量的变化趋势、网络引文顶级域名的分布情况以及网络引文著录规范化等方面进行计量分析,力图揭示我国科技领域研究人员对网络信息资源的利用状况,以期促进网络文献管理的规范化和科学化。     

三个情报学术期刊的分析比较

以中国知网的中国学术期刊网络出版总库和中国引文数据库,以及相关杂志网站为数据来源,对三个情报学术刊物《情报杂志》、《现代情报》和《情报探索》,从期刊定位和栏目设置、进入核心期刊目录情况,以及影响因子、发表文章篇数、稿件来源、引文及引用期刊、资助基金、下载量、被引频次及被引期刊等方面进行分析与比较,并对刊物学术水平与相关指标的关系进行讨论,最后从三个方面对《情报探索》杂志的改进与提高提出意见。     

期刊在引文网络中的信息交流特征研究 —— 基于自被引率、强链接、引文熵和PageRank四个指标的比较分析

本文提出和应用四个指标:期刊自被引率、引文强链接、引文熵和经过改进的PageRank值对期刊互引网络进行综合分析。以Web of Science(2002～2006年)收录的7835种期刊为研究对象,通过四个指标的比较分析,定位整个互引网络中不同类型的"重要"期刊,揭示各个类型期刊在信息交流网络中的不同地位与信息交流特征。     

《情报杂志》2000～2004年网络引文的计量分析

以《情报杂志》5年的载文为样本,对其载文的网络引文中所反映出的信息,如网络引文文献数量、域名、著录格式、有效链接功能、作者情况等进行计量分析。     

网络引文量及其可获取性的调查与分析

以14种中外图书情报核心期刊的网络引文的数量、比率、著录格式、域名和类型进行分析来研究网络引文的可获取性,探讨了这些论文对网络资源的利用状况并为规范人们对网络引文的著录提出一些合理的建议。     

Research addressing emerging technological ideas has greater scientific impact

This study empirically examines the association between the extent of emerging technological ideas in a scientific publication and its future scientific impact measured by number of citations. We analyze metadata of scientific publications in three scientific domains: Nano-Enabled Drug Delivery, Synthetic Biology, and Autonomous Vehicles. By employing a bibliometric indicator for identifying and quantifying emerging technological ideas – as derived terms from the titles and abstracts – we measure the extent to which the publication contains emerging technological ideas in each domain. Then, we statistically estimate the size and statistical significance of the relationship between the publication-level technological emergence score and the normalized number of citations accruing to the publication.Our analysis shows that the degree to which a paper contains technologically emerging ideas is positively and strongly associated with its future citation impact in each of the three domains. An additional analysis demonstrates that this relationship holds for citations from other publications, both in the same field as, and in different fields from, the scientific domain of the focal publication. A series of tests for validation further support our argument that the greater the extent to which scientific knowledge (a paper) contains emerging ideas, the bigger its scientific impact. Implications for academic researchers, research policymakers, and firms are discussed.     

期刊文献中的网络信息利用率研究综述——网络引文视角

在调研近年来国内外有关不同学科文献对网络信息利用率研究成果的基础上,从引文数量和引文域名类型的角度对各学科文献对网络信息的利用率进行了比较,并总结了影响不同学科对网络信息利用率的因素,文章最后提出了网络引文研究的前景与存在的问题。     

A link-bridged topic model for cross-domain document classification

Transfer learning utilizes labeled data available from some related domain (source domain) for achieving effective knowledge transformation to the target domain. However, most state-of-the-art cross-domain classification methods treat documents as plain text and ignore the hyperlink (or citation) relationship existing among the documents. In this paper, we propose a novel cross-domain document classification approach called Link-Bridged Topic model (LBT). LBT consists of two key steps. Firstly, LBT utilizes an auxiliary link network to discover the direct or indirect co-citation relationship among documents by embedding the background knowledge into a graph kernel. The mined co-citation relationship is leveraged to bridge the gap across different domains. Secondly, LBT simultaneously combines the content information and link structures into a unified latent topic model. The model is based on an assumption that the documents of source and target domains share some common topics from the point of view of both content information and link structure. By mapping both domains data into the latent topic spaces, LBT encodes the knowledge about domain commonality and difference as the shared topics with associated differential probabilities. The learned latent topics must be consistent with the source and target data, as well as content and link statistics. Then the shared topics act as the bridge to facilitate knowledge transfer from the source to the target domains. Experiments on different types of datasets show that our algorithm significantly improves the generalization performance of cross-domain document classification.     

Joint deep feature learning and unsupervised visual domain adaptation for cross-domain 3D object retrieval

With the widespread application of 3D capture devices, diverse 3D object datasets from different domains have emerged recently. Consequently, how to obtain the 3D objects from different domains is becoming a significant and challenging task. The existing approaches mainly focus on the task of retrieval from the identical dataset, which significantly constrains their implementation in real-world applications. This paper addresses the cross-domain object retrieval in an unsupervised manner, where the labels of samples from source domain are provided while the labels of samples from target domain are unknown. We propose a joint deep feature learning and visual domain adaptation method (Deep-VDA) to solve the cross-domain 3D object retrieval problem by the end-to-end learning. Specifically, benefiting from the advantages of deep learning networks, Deep-VDA employs MVCNN for deep feature extraction and domain alignment for unsupervised domain adaptation. The framework can enable the statistical and geometric shift between domains to be minimized in an unsupervised manner, which is accomplished by preserving both common and unique characteristics of each domain. Deep-VDA can improve the robustness of object features from different domains, which is important to maintain remarkable retrieval performance.     

Categorical coherence, classification volatility and examiner-added citations

Patent applicants and examiners do not always have the same views about what constitutes a patent's relevant prior art. We propose that the processes of categorization and classification variably shape the interface between applicants and examiners by influencing assessments of similarity between new and existing technologies. Some inventions sit in technological domains that cut across the categorical boundaries implied by examiners’ patterns of specialization. Some sit in domains wherein the classification system that guides examiner searches is more volatile. In either of these circumstances, heightened ambiguity leads to more examiner-added citations on patents that are granted. We test and confirm our predictions in a sample of patents granted to semiconductor firms in 2005.     

学术论文的关键词与引文共现关系分析及实证研究

文章设计了用于文献不同特征交叉共现关系的分析算法,并将这种方法应用于关键词与引文的交叉共现关系研究。实验以计量研究为实证领域,通过微观的交叉共现对、宏观的可视共现网络这两个层次,对关键词与引文的交叉共现结果进行分析与解释,发现关键词与引文的交叉共现分析可用于发现领域的研究方向、识别论文的研究主题、查找某一研究方向的经典文献等。     

我国数字图书馆研究的国际影响力——基于Scopus的近十年文献分析

运用文献计量学方法,选取近十年(2001-2010)来被Scopus数据库收录的数字图书馆研究的相关文献,对中外作者发表的文献数量、主题词、文献类型、出版物、作者和研究机构、文献被引等进行了分析和对比,以明确我国在数字图书馆研究领域的国际影响力。结果发现我国作者发表的数字图书馆文献数量近三年增长迅速,但是会议论文过多,引用率低下,文献总体国际影响力较低。同时对相关原因进行了探讨,以便为未来我国数字图书馆学的发展提供参考。     

国内机构知识库研究的计量与内容分析

为系统了解国内机构知识库研究现状与趋势,采用文献计量法、比较分析法等,以CSSCI收录的机构知识库来源文献和被引文献数据为基础,从来源文献、引文情况及被引情况3个角度,对载文情况、引文概况、引文语种、引文类型、作者情况、期刊情况、被引情况、被引成果等多个方面进行统计、分析,并通过对这些文献的阅读、关键词分析和国内外有关研究情况的比较,针对国内机构知识库研究的问题,提出了4项建议。     

Bibliometric analysis of CiteSeer data for countries

This article describes the results of our analysis of the data from the CiteSeer digital library. First, we examined the data from the point of view of source top-level Internet domains from which the data were collected. Second, we measured country shares in publications indexed by CiteSeer and compared them to those based on mainstream bibliographic data from the Web of Science and Scopus. And third, we concentrated on analyzing publications and their citations aggregated by countries. This way, we generated rankings of the most influential countries in computer science using several non-recursive as well as recursive methods such as citation counts or PageRank. We conclude that even if East Asian countries are underrepresented in CiteSeer, its data may well be used along with other conventional bibliographic databases for comparing the computer science research productivity and performance of countries.