根据关键词不完全共现，下面绘制了《情报学报》 2012 年上半年发文关键词的共现图。 笔者将其归纳为七个方面： A 计量学文献（文献计量、科学计量及信息计量）。这个版块发文数量最多。即有利用传统文献计量方法对某个领域研究（如信息行为研究的文献计量），也有利用知识图谱、引文分析、共词分析等对某领域研究热点（如情报学）或某主题研究热点（如基因工程、人民币汇率）的分析。 B 本体。本体是近年来情报学领域一直关注的一个研究热点。有几篇文章将语义分析、大众标注等相结合，尝试了本体的应用研究。 C 竞争情报。竞争情报是情报学领域又一个倍受关注的话题。在一篇文献是竞争情报领域的文献综述，另两篇探讨了动态竞争情报、竞争情报系统。 D 用户研究与信息推荐。用户是情报学研究过程中，一直会关注和研究对象。有几篇文章对用户的心智模型、用户体验、认知网格等进行了研究；还有几篇探讨了个性化信息推荐。 E 信息检索与信息查询。有几篇文献探讨了查询优化、查询扩展、专家检索、跨语言信息检索；还有几篇文章将信息检索与网络学习、信息服务相关问题进行了研究。 F 文本与文档相似度、语义学习等。 这个版块是情报学领域利用计算机科学等相关知识对词汇、文档及网页为研究对象进行的相关研究。 G 最后一个版块的内容较为分散，涉及到电子政务、搜索引擎、信息经济、网络事件、信息采集等方面。 另外，从图中还可以看出情报学领域目前使用较多的研究方法，如聚类、社会网络分析、共词分析，共现分析、多维尺度分析等。还有值得注意的是语义层面的研究引起了不少研究者的注意。如 B 、 E 、 F 中都有包含语义的词汇。 利用这种方法绘制的“关键词共现图”受到作者关键选择的影响较大。如有的作者使用“科学知识图谱”，有的使用“知识图谱”。这就会导致两个词相关的词无法形成一个连通的网络，而实际上它们是连通的。在以后数据处理过程中，笔者将在这方法进行一些先期的处理，以绘制更加“语义化”的图形。
情 报 学 报 ISSN 1000-0135 第 30 卷 第 8 期 787-795 ， 2011 年 8 月 JOURNAL OF THE CHINA SOCIETY FOR SCIENTIFIC AND TECHNICAL INFORMATION ISSN 1000-0135 Vol.30 No.8 ， 787-795 1. Evolution of Knowledge Networks (I) ： Growth and Obsolescence Ma Feicheng and Liu Xiang (Center for Studies of Information Resources of Wuhan University, Wuhan, 430072) Abstract: For the analysis of topology and obsolescence of knowledge under different growth patterns, we constructed an evolution model of knowledge networks. A general function analysis and simulation had been done, it was found that the degree distribution had no relation to the growth pattern and the diachronic citation of vertex decreased, when the growth ratio of knowledge was convergence function, such as liner function; On the other hand, the exponent of degree distribution was small and the diachronic citation was increased when the growth function was transpire function, like exponent function. The faster the rapid of growth, the flatter the degree distribution it was, which also means the higher efficiency of knowledge utilization. Keywords: knowledge, complex networks, evolution, obsolescence, model 2. Collaborative Recommendation Using Smoothing Clustering Based on User Information Matrix Chang Fuyang, Xu Kan and Lin Hongfei (School of Computer Science and Technology, Dalian University of Technology, Dalian 116024) Abstract: Collaborative recommendation technology can help people find something interesting in the e ? commerce business field. In collaborative recommendation, there is a common way to generate recommendation called nearest neighbor method. With the increase of commodity quantity, the ratio of useful data is decreasing. In order to solve the sparse problem, we collect and discrete user information on the basis of ordinary score data, then we convert user information to a 0 ? 1 vector. We compute the N ? nearest neighbors from the user information matrix and smooth the it using the k ? NN. We cluster the user rating matrix to predict the score. The experiment results show that the approach of rating and discretion the user information can improves the predicting score precision. Keywords: collaborative recommendation, user information, data smoothing, rating cluster 3. Contextual Recommendation ? oriented User Preference Drift Recognition Based on Hypergraph Model Cai Shuqin1, Hu Muhai2, Ye Bo3 and Ma Yutao1 （ 1.Institute of Enterprise Business Intelligence Engineering, Huazhong University of Science and Technology, Wuhan 430074 ； 2.Management School, Wuhan Textile University, Wuhan 430074; 3.Guangxi Technology Information Net Center ， Nanning 530012 ） Abstract: User preference drift recognition is one of the keys to update user profile and keep the description precision of users preference. With the quick development of mobile commerce, such recognition was paid great attention recently. However, most of researches based on clustering are insufficient for the treatment of item objects where weak N-ary associations exist. In this paper, through the analysis of contextual recommendation, a hypergraph model of contextual items is proposed, and the similarity between a pair of items, a pair of item clusters and user preference drift degree are defined. Based on above related definitions, a method to measure preference drift is constructed which is based on two stages hierarchical clustering framework and in combination with Multilevel k-way Hypergraph Partitioning arithmetic. Finally the time complexity and application mechanism of the method are discussed, the usefulness of the method is also verified by two groups of experiments. Keywords: hypergraph, contextual recommendation, preference drift 4. Fuzzy Clustering Model and Algorithm Based on Rate Distortion Theory Guo Chonghui and Zhang Yanchang (Institute of Systems Engineering, Dalian University of Technology, Dalian 116024 ) Abstract: Clustering is considered as a process of lossy compression from an information theory perspective in this paper. Firstly an optimization model of fuzzy clustering is built by using the rate distortion theory. Comparing to the classic fuzzy clustering model, the new model introduces a new index in the objective function which describes the complexity of clustering process. In order to estimate the number of clusters, a new cluster validity index is also proposed. Then the fuzzy clustering algorithm based on rate distortion theory is obtained by solving the optimization model. Finally some numerical experiments are made to compare the fuzzy clustering algorithm based on rate distortion theory with fuzzy c ? means. The experimental results indicate that the fuzzy clustering algorithm based on rate distortion theory can estimate the number of clusters automatically and it also has less running time than fuzzy c ? means. Moreover, membership assignments of the proposed algorithm based on rate distortion theory are less confused than fuzzy c-means, which makes the result more definite and reliable. Keywords: fuzzy clustering ， rate distortion theory ， mutual information ， number of clusters 5. Study on Clustering of Retrieval Results Based on Co-occurrence Analysis of Keywords Li Fenglin and He Zhoufang (Center for Studies of Information Resources of Wuhan University, Wuhan 430072) Abstract: The continuous growth in the size of the Internet is creating difficulties for improving efficiency of information retrieval. First of all, this paper extracts the keywords from each document through a specific algorithm. Secondly, it has applied statistical techniques to measure the quantities of co ? occurrence keywords for forming the label matrix of them, and finally agglomerated them into higher ? level clusters by hierarchical clustering algorithm in order to classify the results which return from the source research engine. The view of retrieval results clustering can help the user quickly and efficiently navigate the results of a query at a topic level and locate the relevant information. Compared with Lingo, the experimental results show that the labels generated by our algorithm are of more readability and generality. What ’ s more, F ? measure index also shows that our algorithm has improved the quality of text clustering to some extent. Keywords: keywords, co-occurrence, clustering, retrieval results 6. A Novel Classification Model Based on Relaxed Conservative Inference Rule for Incomplete Data Qi Ruihua1,2 and Yang Deli2 (1.Modern Education Technology Center, Dalian University of Foreign Language , Dalian 116044 ； 2.Institute of System Engineering, School of Management, Dalian University of Technology, Dalian 116024) Abstract: To solve the problem of declining proportion of clear samples in the tofal when using Naive Credal Classifier, this paper improves conservative inference rule, and proposes an incomplete data classification model based on relaxed conservative inference rule. Simulation results of comparative experiment with Naive Bayesian Classifier and Naive Credal Classifier verify the effectiveness of this classification model. Besides, the style identification as the application background, comparative experimental results further show that this classifier has better overall performance on the style identification data set. Keywords: classification, incomplete data, interval advantage 7. Research on Semantic Text Mining Based on Domain Ontology Zhang Yufeng and He Chao （ Center for Studies of Information Resources of Wuhan University, Wuhan 430072 ） Abstract: In order to improve the depth and accuracy of text mining, a semantic text mining model based on domain ontology is proposed. In this model, semantic role labeling is applied to semantic analysis so that the semantic relations can be extracted accurately. For the defect of traditional knowledge mining algorithms that can not effectively mine semantic meta database, an association patterns mining algorithm based on semantic is designed and used to acquire the deep semantic association patterns from semantic meta database. Experimental results show that the model can mine deep semantic knowledge from text database. The pattern got has great potential applications, and the algorithm designed has strong adaptability and scalability. Keywords: semantic text mining, domain ontology, semantic patterns 8. Application Research on Improved PSO Algorithm for Data Prediction Mining Wang Xiaojia, Yang Shanlin and Xu Dayu (Hefei University of Technology, Key Laboratory of Process Optimization and Intelligent Decision ? making, Ministry of Education, Hefei 230009) Abstract: In order to solve the problem of prematurity and tendancy to fall into local convergence in particle swarm optimization algorithm, this paper proposed an improved particle swarm optimization algorithm that is able to overcome prematurity. Extreme disturbances and adaptive adjustment factor were added to the standard PSO algorithm. Making the algorithm can jump out of local optimum easily. It also analyzed the limitations of gray model GM (1,1).So a selfadaptive PSO algorithm with disturbed extremum called AdPSO is presented. Utilizing the new model for data mining prediction. Finally, an example is used to validate the proposed method. Example shows that this model has higher prediction accuracy. Keywords: particle swarm optimization algorithm, GM(1,1) model, AdPSO-GM model, forecasting mining 9. A Tag Ranking Method Based on HITS and Random Walk Wang Zhaopeng1, Hu Xia2, Ni Ning3 and Wang Can1 （ 1.College of Computer Science, Zhejiang University, Hangzhou 310027; 2.Hangzhou Science and Technology Information Research Institute, Hangzhou 310001; 3.Information Technology Department, Zhejiang Vocational College of Commerce, Hangzhou 310012 ） Abstract: With the rise of Web 2.0 applications, a new trend in information science, namely the evolution from “organizing document” to “organizing knowledge”, is looming on the horizon. One of important Web 2.0 applications, social tag, is making this trend a reality by adding meaningful annotations to Web pages. However, existing tag ranking methods are not efficient in knowledge organization. To improve tag ranking performance, this paper proposes a new ranking algorithm by utilizing relationships among users, tags and Web documents in a tripartite collaborative tagging model. By combining HITS and random walk, we effectively exploit the mutual reinforcement between quality users and quality tags and retrieve related tags by measuring similarity between tags. Experimental results on Delicious dataset demonstrate the effectiveness of our algorithm. Keywords: tag, ranking, HITS, random walk 10. Research on the Recognition of Business Organizations Names in Internet Zhao Jie1,2, Liu Yanhong3 and Jin Peiquan3 (1.School of Business Administration, Anhui University, Hefei 230029; 2.School of Management, University of Science and Technology of China, Hefei 230026; 3.School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026) Abstract: Internet has been one of the major sources for enterprises and organizations to acquire competitive intelligence. And many enterprises have shown urgent requirements on building a Web ? based system to acquire competitor intelligence. In such a Web ? based competitor intelligence system, a fundamental issue is to recognize business organizations’ names in Internet, because it is the basis of identifying competitors and extracting further intelligence from the Web. In this paper, we present a new approach to recognizing business organizations in Internet, which considers the semantic relationship between business organizations’ names and their context in Web pages and recognizes organizations’ names based on an integration of semantic annotation and the Hidden Markov Model (HMM). We conduct an experiment on a real dataset consisting of a large number of Chinese Web pages and evaluate the performance of our approach as well as three competitor algorithms including CHMM, MEM, and SVM, with respect to recall, precision, and F ? measure. The results show that our new approach improves the effectiveness of the reorganization of business organizations ’ names. Meanwhile, it is a general ? purposed algorithm and can suit different types of tasks on business organizations recognition. Keywords: competitive intelligence, internet, business organization, hidden Markov model 11. Network Characteristics of Chinese Scientific and Technical Vocabulary System Hu Changai and Zhu Lijun （ Institute of Scientific and Technical Information of China, Beijing 100038 ） Abstract: The evaluation of the traditional knowledge organization systems lacks of macroeconomic measure, works out very late, and cann’t reveal dynamic process. Based on complex network theory, this paper has analyzed the network properties of the Chinese science and technology vocabulary system from the aspects of basic characteristics, dynamic characteristics and robustness. The analysis of basic characteristics shows that the Chinese science and technology vocabulary system is a small world and scale free network, and it has good connexity. But there are errors and multiplicity; The analysis of dynamic property shows that the system is much more small ? world, while the network's performance should be further improved. The analysis of robustness shows that the system is much robust. So that the suggestion has been advanced that the vocabulary system should be constructed under the direction, and the importance should be attached to each and every vocabulary to ensure the connectivity of the system. Keywords: Chinese scientific and technical vocabulary system, complex network, small world, scale ? free, robustness 12. Research on Personalized Cross-language Academic Search Pang Guansong1, Zhang Lisha2 and Jiang Shengyi3 （ 1.School of Management, Guangdong University of Foreign Studies, Guangzhou ， 510006 ； 2.Cardiff Business School, Cardiff University, CF10 3EU, Cardiff, United Kingdom ； 3.School of Informatics, Guangdong University of Foreign Studies, Guangzhou ， 510420 ） Abstract: The academic search engine is a domain ? oriented search engine. However, due to its lack of personalized services, there appeared the problem of inefficiency in literature retrieval and insufficient usage of massive digital academic resource. This paper employs Google translation, presents a Chinese, English, Russia, French and Spanish cross ? language academic search engine based on machine translation. On the foundation of cross ? language academic search, we research on personalized information retrieval techniques, propose a personalized information retrieval approach based on clustering: based on the click behavior of the clusters achieved by search results clustering, generates and updates user real ? time profile, employs cosine formula compute the similarities between the user real ? time profile and search results, finally personalized resorts the search results based on the similarities. The experimental results show that the proposed approach has its effectiveness and users acceptance. Keywords: cross-language information retrieval ， personalized information retrieval ， one pass clustering ， academic search ， user click behavior 13. Network Based Users Book?Loan Behavior Analysis ： A Case Study of Peking University Library Yan Fei1, Zhang Ming1, Sun Tao1 and Xiao Long2 （ 1.School of EECS, Peking University, Beijing 100871; 2. Library of Peking University, Beijing 100871 ） Abstract: Book loan is the most important service in libraries. Taking Peking University as an example, almost every student has borrowed books from the library. Hence, it is essential to understand users ′ book ? loan behaviors, and provide better user ? oriented services based on the understandings. There exist two kinds of networks in libraries: book ? borrowing network and co ? borrowing network. In the book ? borrowing network, a user and a book are connected if the user borrowed the book. Meanwhile, in the co ? borrowing network, two users are connected if they borrowed same books. The latter can also be regarded as a knowledge sharing network. In the paper, we analyze users ′ book ? loan behaviors in these two networks, gain new understandings from users ′ behaviors, and apply the analysis results to promote library services. Our research exactly goes as the trend of Library 2.0. Keywords: user behavior analysis, social network analysis, digital library, log mining 14. Visualization Analysis of the Research Fronts Based on CiteSpaceII Yang Liangxuan, Li Zili and Wang Hao (School of Information System and Management ， National University of Defense Technology ， Changsha 410073 ） Abstract: Research Fronts is emerging thematic trends and surges of new topics, according to find out the research fronts, can provide for the researchers the newest information which he or she wants to know. Based on this, first, we introduced the existed methods to detect the research fronts in briefly; and then, used the visualization software CiteSpaceII to description the relationships of co ? citation, the critical articles and important researchers would be listed. In addition, we also discussed the shortage of the software, and forecast the future application of the research fronts. Keywords: research fronts, fronts analysis, CiteSpaceII 15. Study on Focuses in Pervasive Computing Based on National Top level Domains Huang Lucheng and Zhao Pan (School of Economics and Management, Beijing University of Technology, Beijing 100124) Abstract: With the development of network technology and the massive data, the network information has become the community can not ignore the important information resource, which not only contains a lot of information and technology related, but also subtle to guide and influence technology development. According to status of current research in this area, we propose idea analyzing focus technology and development direction in future using national domain name in Top ? level domain based on Internet information. Using the hierarchical clustering method, through comparing countries in one group and between groups, we can discover various countries current universally concerned hot technology and technology development direction in future, and use pervasive computing technology as an example for empirical analysis. Keywords: national Top ? level domain, hot technology, cluster analysis, pervasive computing technology
情 报 学 报 ISSN 1000-0135 第 30 卷 第 7 期 675-681 ， 2011 年 7 月 JOURNAL OF THE CHINA SOCIETY FOR SCIENTIFIC AND TECHNICAL INFORMATION ISSN 1000-0135 Vol.30 No.7 ， 675-681 July 2011 1.Study on Potential Knowledge Discovery from Electronic Medical Records Based on Semantic Triple Wen Youkui1,2 and Jiao Yuying1 (1.School of Information Management of Wuhan University, Wuhan 430072 ； 2.School of Economy and Management, Xidian University, Xi’an 710071) Abstract: Electronic medical records (EMR) is a challenging task of medical informatics (MI) in order to solve the expression, organization, application in the field of medical information. Information of medical records has specific semantic requirements, mainly in professional represents of medical information and truth of the actual medical procedure. Electronic medical records can not now express dynamic semantics relationship between the concepts of pathological; it is difficult to realize semantic based information retrieval and potential pathological findings. For this reason, we developed a electronic medical records of potential knowledge discovery systems based on knowledge element, which extract electronic medical records into the knowledge element of semantic triples form, to solve semantic reasoning and potential disease problems of the electronic medical records, to implement integrated system of treatment cases for diagnosis, instrumentation detection, treatment options. Experiment proved the system have some guidance for diseases treatment, teaching and research. Keywords: electronic medical records, knowledge discovery, knowledge element, semantic triple 2. Research on Multi-perspectives Topic Constructing Wu Qingqiang1 ， 2 and Zhang Xiaolin2 （ 1.Institute of Scientific and Technical Information of China, Beijing 100038 ； 3.Library of Chinese Academy of Sciences, Beijing 100094 ） Abstract: In this article, the authors conduct the research on the multi-perspectives access to topic structure. Firstly, an experiment is used to discover that there are some obvious differences between the two topic structures generated by the variant analysis and the co-word analysis, and both of which are a reflection of the existing topic structure. According to the characteristics of variant analysis and co-word analysis, this dissertation adopts the relationship integration, the process integration and the result integration to obtain the topic structure in multi-perspectives. In the relationship integration, three different functions including the linear function, the Max function and the co-variant are adopted. The tests have been carried out in terms of the topic class levels, the relationship of words and the relationship in the topic class to verify their adaptation to the topic structure in multi-perspectives. Keywords: topic constructure, multi-perspectives topic constructing, co-word analysis, variant analysis 3. Research on Semantic Reasoning with Ontology and Rules Tang Xiaobo and Jin Zhongming (Center for Studies of Information Resources of Wuhan University, Wuhan 430072) Abstract: To solve the information share and integrating for related concept of ontology, find the semantic relationship among ontologies, this paper proposes the semantic reasoning model ORRM with ontology and rules combined, establish the family ontology FO. Semantic reasoning focus on two layers, the first layer uses Racer to reason based on description logic, testing ontology conflict. The second layer uses concepts and attributes presented by ontology for member rule base, using SWRL and Jess as inference engine. The new ontology exported by the model adds the semantic relationship between the concepts, makes implicit knowledge explicit and perfects the contents of ontology knowledge base. In the fields of semantic web, the utilization of ontology knowledge is improved by the application of the model. Keywords: domain ontology ， description logic ， SWRL ， Jess ， semantic reasoning 4. Language Model and Its Application to Information Retrieval Su Sui, Lin Yuan and Lin Hongfei (Laboratory of Information Retrieval, School of Computer, Dalian University of Technology, Dalian 116024) Abstract: Language modeling approach to information retrieval is promising and challenging because of its favorable foundations in statistical theory, which can deduce other classical retrieval models easily. However the recent researches on language modeling approach focus on the task of single language retrieval, and there are few studies examining its effectiveness on cross language retrieval. In this paper, we apply language modeling approach to the task of cross language retrieval based on introducing the retrieval approaches of language models. This paper presents two cross language retrieval model: statistical translation model and cross language relevance language model, and also analyzes their effectiveness. Keywords: statistical language model ， cross language information retrieval ， relevance model 5. A Collaborative Filtering Recommendation Algorithm Based on Item-Class Preference Leng Yajun, Liang Changyong, Zhang Enqiao and Qi Xiaowen (School of Management, Hefei University of Technology, Hefei 230009) Abstract: Currently collaborative filtering is the most successful and widely used recommendation technology in recommender systems. However, with the development of E-commerce, the magnitudes of users and commodities grow rapidly, which results in the extreme sparsity of user rating data. The method of searching for nearest neighbors in traditional collaborative filtering algorithm works poor in this situation, which makes the quality of the recommender systems decrease dramatically. To address this issue, a collaborative filtering recommendation algorithm based on item-class preference is proposed. The proposed algorithm first finds out a set of candidate neighbors who are similar to the active user in item-class preference. The candidate neighbors have similar interest and more co-rated items with the active user. Then the algorithm identifies some nearest neighbors in the candidate neighbor set, which eliminates the interference of the users who have few co-rated items with the active user, and enhances the accuracy of searching for nearest neighbors. The experimental results show that the proposed algorithm can efficiently improve recommendation quality. Keywords: recommender system, collaborative filtering, item-class preference, similarity 6. A Study on the Method of Mobile Content Recommendation Based on Frequent Marked Lattice Cai Shuqin1, Zhang Yu2, Hu Muhai1 and Xiao Quan3 （ 1.School of Management, Huazhong University of Science and Technology, Wuhan, Hubei, 430074 ； 2.School of Management, Wuhan Textile University, Wuhan 430073 ； 3.School of Information Technology, Jiangxi University of Finance Economics, Nanchang 330013 ） Abstract: In existing mobile content service systems, There are few studies is still quite rare on automatic situation service rule construction. Hence, a method is proposed that the semantic association rules between situations and preferences are built by quantitative frequent marked lattice based on ontological model. The method provides a solution to the problems of rule collision and context data availability. Frequent Marked Lattice further reduces needed node number for rule generation compared to frequent concept lattice and it is more convenient to extract different rules and calculate related parameters. The construction algorithm of FML and priority mechanism of rule extraction are designed. The validity of the algorithm is verified by experiment and an analysis is performed compared with the related works. Keywords: mobile content recommendation ， situation ， context ， ontology ， frequent marked lattice 7. Research on Knowledge Transfer Behavior of individuals Based on Cellular Automata Wu Jiangning, Liu Na and Xuan Zhaoguo (Institute of Systems Engineering, Dalian University of Technology, Dalian 116024) Abstract: Knowledge exchange and transfer are key means for the organization to acquire knowledge and hence promote its knowledge stock and competitive competence, which has attracted more attention from many knowledge managers. This paper starts with this issue and focuses on the different learning styles of individuals within the organization. The individuals involved are divided into two types, i.e. active learner or passive learner, whose learning attitude, behavior and knowledge stock will be changed during the process of knowledge exchange. The knowledge to be exchanged is expressed by a multi-dimensional vector. Hereby, a cellular automata knowledge transfer model is proposed to study the influence of structure of neighborhood, the ratio of initial active learners, the people movement and expert introduction mechanisms on the performance of knowledge transfer processes by using two kinds of rules which are the state update rule and the knowledge exchange rule respectively. Simulation results show that the local interactions between individuals at the micro-level reveal some complex properties like self-organization at the macro-level within the organization. The structure of Moore neighborhood is more effective on the knowledge exchange and transfer within the organization, and the growth of the average knowledge stock is going up with the increase of the ratio of initial active learners. Both the people movement and the expert introduction mechanisms can improve the performance of knowledge transfer and knowledge stocks. Besides, the growth of the average knowledge stock is proportional to the self-learning ability of individuals. Keywords: knowledge transfer, cellular automata, people movement, expert introduction 8. Evaluating Influence of Sampling Methods Upon Social Network Analysis Tan Xiaojie, Wu Kewen, Zhao Yuxiang, Zhu Qinghua and Wang Qianjun (Department of Information Management, Nanjing University, Nanjing 210093) Abstract: Social Network Analysis(SNA), a quantitative research method which aims at analyzing the structure and various properties of target network, has been applied in many academic fields. However, those existing studies emphasize more on the explanations of SNA indexes, rather than evaluating the impact of process elements to the research conclusion. This study selected four classic sampling methods (node sampling, link sampling, depth first sampling and snowball sampling) as the research object, and briefly analyzed the impact of different sampling methods to five typical SNA indexes (diameter, average distance, clustering coefficient, degree centrality and betweenness centrality) in scale free networks, which can be helpful to formalize the research process of implementing SNA. Keywords: social network analysis, sampling method, sample error, snowball sampling 9. A Study on the Scientific Research Collaboration Network of “ 985 Project” Universities in China Qiu Junping and Wen Fangfang (The Research Center of Chinese Science Evaluation, Hubei Wuhan 430072) Abstract: Universities have become the main component in the national scientific innovation system. As the collaboration between different universities strengthens, the study on the scientific research collaboration is of increasingly vital value and importance. This paper chooses the “985 Project” universities as the sample to study on their scientific research collaboration relationship from the perspective of co-authorship based on the methods and tools of Social Network Analysis. The research results illustrate that the 39 universities has already established the primary scientific research collaboration, but the intensity still waits for enhancement. Besides, there exists significant correlation between scientific output and scientific collaboration. It means that to some extent to strengthen collaboration intensity can improve the quantity of universities’ scientific output. Keywords: scientific collaboration, collaboration relationship, social network analysis, co-authorship 10. Patent Mapping of Technology Competition among Fortune 500 Enterprises Based on Global Patent Co-citation Analysis Wang Xianwen1 ， 2 ， Ding Kun1 ， 2 and Zhang Xi1 (1.Faculty of Humanities and Social Sciences, Dalian University of Technology, Dalian 116085 ； 2.Joint Institute for the Study of Knowledge Visualization and Scientific Discovery ， DUT?Drexel ） Abstract: Patent citations are generally used to provide support for specific statements of technology competition, when patentometrics has become one kind of main method to analyze competitive technical intelligence for companies. In this paper, the authors choose Fortune 500 enterprises, released in 2009, as research objects. Using patent data (2000~2009) from Derwent Innovation Index, employing full network methods in social network analysis, the authors construct the global patent co-citation matrix of Fortune 500 companies based on the patent forward citation, and conduct patent mapping analysis using information visualization technology and social network analysis method, including clustering distribution, Kernel density, and co-citation network analysis, to study the technology clusters, technology competition structure of Fortune 500, and to find the pivotal enterprises in the technology competition network. Keywords: forward citation ， patent co-citation ， technology competition ， patent mapping ， global co-citation 11. Co-word Analysis on the Hotspots in Chinese Library and Information Science for the Last Decade Wang Hong (Hubei University of Automotive Technology, Shiyan 442002) Abstract: Five years as a period ， this paper uses the co-word analysis method to perform cluster analysis and cluster relationship analysis to the high frequency keywords of eight core journals of Chinese Library and Information Science （ abbreviation LIS ） from 1998 to 2007. At the same time, the paper draws the relation table of clustering results and drafts the relationship figure of cluster, and systematically analyzes the research hotspots of Chinese LIS in different period, in order to reveal microscopically the research hotspots of Chinese LIS in different period. To sum up, the research hotspots of Chinese LIS has a certain characteristics of inheritance, continuity, stability, expansibility and variability. Keywords: co-word analysis, library and information science, cluster analysis, cluster relationship analysis 12. The Study on Information Visualization Technology in Medical Information Analysis Xiong Jun and Qiu Xiao （ School of Medicine and Health Management, Hangzhou Normal University, Hangzhou 310036 ） Abstract: We made the scientific map basis on the citation analysis, co-citation analysis, cluster analysis and social network analysis, and with the help of Citespace, Bibexcel, Pajek and Ucinet . Discussion on the use Information visualization techniques in medical information analysis. Through the knowledge mapping, we can determine the seven core journals. Use of network analysis methods analysis the social position and role ,which include the core journals, reference texts and the author. Through the keywords and Noun phrases, we draw the knowledge mapping of public health and preventive medicine, and reveal the research topic and hot from 2005?2009. I hope it can help someone who want to apply the information visualization technology in Medicine information. Keywords: information visualization ， knowledge mapping ， citation analysis ， cluster analysis ， public health and preventive medicine
《情报学报》被以下数据库或检索系统收录： 一、国外检索系统 1 ． INSPEC （科学文摘）， published by the Institution of Engineering and Technology (IET), and formerly by the Institution of Electrical Engineers (IEE) ， 网址： http://www.theiet.org/publishing/inspec/ 2 ． LISA （图书馆和信息科学文摘）， 网址： www.csa.com/factsheets/ lisa -set-c.php 备注：通过 ProQuest 亦可查到。 3 ． PЖ 俄罗斯《文摘杂志》， 网址： 备注：《情报学报》是中国图书情报领域唯一被俄罗斯《文摘杂志》收录的期刊。 二、中国索引 CSTPCD 中国科技论文与引文数据库（中国科学技术信息研究所）， 网址为： http://www.istic.ac.cn/tabid/640/default.aspx CSSCI 中文社会科学引文索引（南京大学）， 网址为： http://cssci.nju.edu.cn/ 中国学术期刊文摘（中国科学技术协会）。 网址为： http://www.csciabs.org.cn/ 备注：《情报学报》是中国图书情报领域唯一被《中国学术期刊文摘》收录的期刊。 三、全文数据库 中国学术期刊网络出版总库（中国知网，即清华同方），（主站提供 2003 年之前的全文， 2003 年以后只提供题录及摘要信息） 网址为： http://acad.cnki.net/Kns55/brief/result.aspx?dbPrefix=CJFQ 数字化期刊全文数据库（万方数据） 网址为： http://www.wanfangdata.com.cn/ 四、尚未收录的索引 SCI 科学引文索引 SSCI 社会科学引文索引 EI 工程索引 本刊的英文名称为： Journal of the China Society for Scientific and Technical Information ，简写为 JCSSTI ，名称类似于美国的 Journal of the American Society for Information Science and Technology ，简写为 JASIST ，国家不同，单词顺序有别。 本刊的 ISSN 为： 1000-0135
标签: 情报学报
