861 Combing Multiple Platforms of Online Reviews to Measure the Comprehensive Impact of Books Zhang Chengzhi, Tong Tiantian, Zhou Qingqing DOI: 10.3772/j.issn.1000-0135.2018.09.001 Currently, online book reviews are abundant and widely available on e-commerce and social network websites. Mining these reviews is important for comprehensively evaluating books and persuading customers to make purchasing decisions. However, most prior research based on book reviews mainly focused on a single review platform instead of using review data from multiple platforms, resulting, to some extent, in a one-sided evaluation. Hence, based on the quantitative analysis of book reviews across different platforms, we put forth one method to measure the comprehensive impacts of books utilizing multi-source data, which provides fresh ideas for book evaluation by collecting and integrating book reviews from social networking platforms and e-commerce websites. In this study, by taking 348 books in four subjects as an example and integrating multi-source reviews of them from an aspect perspective, we analyzed the differences between book evaluation results obtained through different integration strategies. In the end, a comparison between the results obtained by our method and traditional methods based on a single platform shows that combing multiple reviews can evaluate a book’s impact more comprehensively and avoid the limitations of traditional methods. 2018 Vol. 37 (9): 861-873 [Abstract] ( 223 ) HTML (1 KB)  PDF (644 KB)  ( 1386 )

874 A New Method for Journal Evaluation—Analysis Based on the Authors’ Bios in Journal Articles Song Xiaochen, Li Menghao, Zhou Liang DOI: 10.3772/j.issn.1000-0135.2018.09.002 Researchers categorize the various methods of assessing journal quality into two approaches—bibliometric methods and expert assessment—each having its own advantages. This paper proposes a new method to measure the academic influence of journals. The method takes the authors of authoritative journal articles as the starting point and collects the personal profiles/biographies written in these articles that disclose the main journals in which such authors used to publish. We organize and process the ranking of journals’ importance in a certain field and calculate the authors’ Vote Score (V-score) on every journal. On the basis of this V-score, we can assess the impact of a journal in such a field. In addition, the efficacy of the method is tested with examples based on leading journals in the field of Information Systems. This method combines the advantages of the two traditional methods, and compensates for their deficiencies to a certain extent, which has certain practical significance. 2018 Vol. 37 (9): 874-881 [Abstract] ( 170 ) HTML (1 KB)  PDF (346 KB)  ( 627 )

882 Study on Citation Characteristics of SEP from an Ecological Perspective Li Rui, Zhou Wei, Wang Xue DOI: 10.3772/j.issn.1000-0135.2018.09.003 Standard essential patents (SEP) This abbreviation can be removed from here as this does not appear again in the abstract. are patents contained in industry technical standards, i.e., if a patent is not used, the technical standards will not be enforced. In order to meet the industry technical standards, enterprises must obtain the license of standard-essential patents. Therefore, standard essential patent information is an important competitive intelligence. Based on the analysis of Qualcomm’s patent samples, this paper summarizes the citation characteristics of standard essential patents that differ from ordinary patents, and tests the repeatability of the features found by Huawei and ZTE’s patent samples. Finally, it was found that The use of this word in this context seems inappropriate. Please see if this can be replaced with “results indicate” or “It was found that”the citation characteristics of standard essential patents are analysis and interpretation from the perspective of citation ecology. This paper also aims to provide references based on citation features for patent intelligence workers to identify and foresee “potential” future standard essential patents. 2018 Vol. 37 (9): 882-889 [Abstract] ( 180 ) HTML (1 KB)  PDF (366 KB)  ( 554 )

890 Visualization of Sequential Characteristics of Web Behaviors of College Students Yan Chengxi, Wang Jun DOI: 10.3772/j.issn.1000-0135.2018.09.004 In the information dependent environment, research on web behaviors is an important topic with widespread needs, especially concerning the analysis and research of online behaviors of college students. College students are an important core group and a new force among Chinese netizens. There is great practical significance and social value to exploring and grasping Chinese college students° characters such as user behavior, interests, and needs. Visual analysis can directly display the overall distribution characteristics of user behavior and lay the foundation for further in-depth analyses. In this study, the college students° network access logs are considered as the analysis object to indicate the features of group behavior under multiple time granularities, namely, term, week, and hour. Meanwhile, on the basis of research using Markov chain, Gini-index, H-index, and other feature indicators, this work attempts to reveal college students° online characters of sequential behavior, user interest, and needs in various hour intervals, which provides a scientific reference to understand the nuances of college students' online life and support enterprise personalized services under the big data environment. In particular, H-index is applied to the website ranking algorithm of user interest and shows the value of classical informetrics analysis in the process of analysis and application of online user behavior, thus promoting the integration of different methods in applied information science. 2018 Vol. 37 (9): 890-904 [Abstract] ( 223 ) HTML (1 KB)  PDF (2392 KB)  ( 1284 )

905 Research on Optimization of Scientific Literature Similarity Calculation Based on the Co-citation Feature Han Qing, Zhou Xiaoying DOI: 10.3772/j.issn.1000-0135.2018.09.005 Calculating similarity for scientific literature is the basis of applications such as literature search and literature analysis, and the results have a direct impact on the final effectiveness of the related applications. The co-citation information is an important feature that is different from that of ordinary text. It can effectively represent the correlation between two text inputs. Further, it can be used to improve the validity and reliability of literature similarity calculation. Based on the vector space model, semantic features and co-citation features are introduced into the literature similarity calculation, and a hybrid model is proposed to optimize the similarity calculation of scientific literature. Through the verification of seven research fields, such as university library, online public opinion, and information quality, the results show that the proposed model can make full use of the co-citation features of scientific literature, and thus compensate for the problem of insufficient features in the vector space model and improve the overall performance of scientific literature similarity calculation. 2018 Vol. 37 (9): 905-911 [Abstract] ( 343 ) HTML (1 KB)  PDF (496 KB)  ( 779 )

912 Study of Online Healthy Community User Profile Based on Concept Lattice Zhang Haitao, Cui Yang, Wang Dan, Song Tuo DOI: 10.3772/j.issn.1000-0135.2018.09.006 In this work, the user profiles of online health community users were constructed based on concept lattice to reveal the multidimensional features and behavioral rules of different types of user groups in different contexts. Thus, providing the basis for optimizing community services. Python was used to obtain user data of online health community diabetes circle. Further, an online healthy community user profile concept model was constructed taking three aspects into account: user requirements, user roles, and user behavior. ConExp1.3 tools were used to build user segmentation tag concept lattices, and the user groups were divided into three categories using Hasse diagrams to construct the community group user profiles. Mining association rules were used to identify the behavior of the group users in different situations and obtain a complete picture of the user profiles. Clustering using concept lattices can rank the attributes of each group in a hierarchical manner, thereby making it easy to mine the associations between user attributes. This method has distinct advantages in constructing comprehensive and accurate user group profiles. Hence, serving the community through understanding user groups in depth and ensuring accurate services. 2018 Vol. 37 (9): 912-922 [Abstract] ( 282 ) HTML (1 KB)  PDF (2977 KB)  ( 1411 )

923 A Deep Learning Model and Self-Training Algorithm for Theoretical Terms Extraction Zhao Hong, Wang Fang DOI: 10.3772/j.issn.1000-0135.2018.09.007 Extraction of theoretical terminology from literature is a precondition for more than one research field in information science, such as content analysis of large scale literature and interdisciplinary knowledge transfer revelation. As specific types of named entities, theoretical terms are distributed among many subjects and a large section of published literature, have complex characteristics, and lack large-scale mature corpuses, rendering their extraction quite challenging. To improve the extraction performance and reduce the cost of manual tagging for the training set, a deep learning model for theoretical term extraction was built based on the characteristics of the terms and a self-training algorithm aimed at achieving a weak supervised learning of the model; further, the characteristic construction and tagging method in the model were studied. The validities of the model and the self-training algorithm were verified via experimental comparisons. This study not only provides a more effective method for theoretical term extraction but also provides a reference for the recognition of other named entities. 2018 Vol. 37 (9): 923-938 [Abstract] ( 191 ) HTML (1 KB)  PDF (794 KB)  ( 1132 )

939 An Approach to Identify Emerging Technologies Using Machine Learning: A Case Study of Robotics Zhou Yuan, Liu Yufei, Xue Lan DOI: 10.3772/j.issn.1000-0135.2018.09.008 The traditional bibliometric method uses published articles and patents to improve the reliability and validity of technology foresight. It is a challenging task to extract information from massive datasets owing to limitations posed by manual feature extraction and encoding of knowledge. In addition, the lack of professional expertise leads to inefficient data analysis. In this work, we propose a disruptive technology foresight method based on topic model, which can improve the comprehensiveness and ensure consistent granularity of technology foresight via high throughput processing of massive text datasets. Further, the judgments of the expert group for the five key nodes of the machine learning algorithm improve the recognition abilities of this disruptive technology. In this study, the abstract, published time, and reference data in the Web of Science (WoS) and Thomson Innovation (TI) platforms are extracted to identify the relevant robot field clusters. The results provide useful support for further disruptive technology foresight work. 2018 Vol. 37 (9): 939-955 [Abstract] ( 332 ) HTML (1 KB)  PDF (11009 KB)  ( 493 )

956 Paradigm Shift and Theory Evolution of Information Science (1987-2017) Wang Lin DOI: 10.3772/j.issn.1000-0135.2018.09.009 Research on the theoretical foundations of information science has experienced great changes with respect to the new information infrastructure. This paper reviews the paradigm shifts and theory evolution of information science during 1987-2017. A systematic discussion of the physical paradigm, cognitive paradigm, and domain analytic paradigm of information science is presented. This work points out that the mainstream theory of information science has evolved through the stages of document theory, information management theory, and knowledge theory. Further, an exploration of whether data science can become the next stage of mainstream theory is presented. 2018 Vol. 37 (9): 956-970 [Abstract] ( 339 ) HTML (1 KB)  PDF (731 KB)  ( 948 )