889 Knowledge Service-Oriented Domain Knowledge Organization Hot! Su Xinning DOI: 10.3772/j.issn.1000-0135.2022.09.001 Knowledge organization is the basis of resource construction. It plays an important role in knowledge service efficiency, knowledge reproduction, and resource value reflection. Aiming to resolve concerns in the field of knowledge organization, this study analyzes the current progress of domain knowledge organization from the perspective of knowledge organization theories, methods, and applications. It also elaborates on the research content of domain knowledge by considering the aspects of organization theories, cognitive structures, organization methods, and organization patterns. Notably, this study attempts to solve the current problems in this field by focusing on the theory of domain knowledge organization in relation to knowledge classification, topic clustering, and semantic web; parsing the cognitive structures of domain knowledge from the perspective of classification systems and thematic relevance; advancing domain knowledge organization methods through research on knowledge correlation, knowledge clustering, and semantic web; and exploring domain knowledge organization pattern via knowledge organization patterns of classification organization, topic clustering, and semantic association. Finally, it recommends future research and practice on knowledge organization to further examine the aspects of integration (of multi-type data), semantics (of multi-attribute data), dynamics (of real-time knowledge association), and cross-platform (of systems and databases). 2022 Vol. 41 (9): 889-899 [Abstract] ( 344 ) HTML (1 KB)  PDF (739 KB)  ( 480 )

900 Knowledge Network Construction and Knowledge Measurement of the S&T Innovation Team Hot! Shi Jing, Sun Jianjun DOI: 10.3772/j.issn.1000-0135.2022.09.002 As integrated innovation is increasing in the Science and Technology (S&T) field, teamwork has been identified as an important way to recreate knowledge. Measuring and portraying the knowledge base of S&T innovation teams with precision is of essential significance for interpreting team behaviors and innovation mechanisms, and for managing and developing these teams. This study creatively combines network analysis and knowledge embeddedness to improve traditional knowledge measures and enhance their suitability for S&T innovation teams. Firstly, we applied Maximal Connected Subgraph to identify S&T teams. Thereafter, we expanded the individual member knowledge network base on direct connection and indirect citation, and then collectively calculated a fine-grained team knowledge network. Further, from the positional and relational approaches, two analysis perspectives in network study, we constructed Knowledge Overlap, Knowledge Diversity, and Knowledge Cohesion to measure team knowledge. When calculating, we improved the previous measurements by embedding team knowledge into the whole field-wide knowledge network. Finally, we chose biomedicine as an empirical case where we further discussed and analyzed the team knowledge measurement indicators and their connotations. Positional indicators, Knowledge Overlap and Knowledge Diversity, measure shared knowledge and heterogeneous knowledge respectively, which reflects the composition of team knowledge; the relational indicator, Knowledge Cohesion, reflects the structural consistency and content coherence of knowledge from different teams. In most teams, knowledge nodes are evenly distributed, but the structure of knowledge differs considerably. An inverted “U” relationship can be observed between Knowledge Diversity, which is an important source of team breakthrough creativity, and team performance. Surprisingly, Knowledge Cohesion proved to have a weak effect on performance, despite exhibiting a positive relation with team size. 2022 Vol. 41 (9): 900-914 [Abstract] ( 452 ) HTML (1 KB)  PDF (3757 KB)  ( 418 )

915 Comparison of Process-Oriented Information Interaction Models and Its Enlightenment Hot! Wang Zhihong, Cao Shujin, Liu Yiqun DOI: 10.3772/j.issn.1000-0135.2022.09.003 Several existing process-oriented information interaction models have been proposed in the last few decades. However, to the best of our knowledge, there is no research on systematically comparing and combing these models. Therefore, in this study, by reviewing researches on the explanations of the process approach and methodologies in the fields of philosophy and other social sciences, the components of the general process model are identified. Secondly, a collection of studies on the information interaction model is systematically constructed by combining citation analysis, database retrieval and other methods. Amongst them, the process-oriented models are selected according to the characteristics of the process approach. These models are further classified into four categories, including dynamic evolution model, cognitive process model, behavior process model and work task-based process model in prior to be reviewed. Finally, these models are compared and analyzed from the perspective of the constituent elements of the general process model, including the number of stages, the relationships among the stages, the input and output factors, etc. The corresponding enlightenment is provided from the aspects of the information interaction process and system evaluation metrics and so on. 2022 Vol. 41 (9): 915-929 [Abstract] ( 220 ) HTML (1 KB)  PDF (919 KB)  ( 473 )

930 Dynamic Identification of Emerging Topics in Discipline Based on the Comparison between Different Types of Media: A Method Combining Altmetrics and Citations Hot! Duan Qingfeng, Yan Xuxian, Chen Hong, Liu Dongxia DOI: 10.3772/j.issn.1000-0135.2022.09.004 The timeliness advantage of social networking media helps in identifying emerging scientific topics. However, the evolution of emerging topics in disciplines is relatively different between social media and publishing media in that their altmetrics tend to dominate citations during the period of emergence. We believe that the fast-growing gap occurring between these two indicators is a key basis for distinguishing scientific emerging topics from other topics. As such, we propose a method to recognize the underlying emergent topics in disciplines via comparison among different media. First, by combining altmetrics and citations, we devise the “rgap” indicator (a gap indicating the relative differences of media in terms of the activeness of topics) to conduct sequential comparisons. Second, we employ the Burst Detection Algorithm to detect the burst status of topics, using the sequence of the “rgap” indicator, which can help identify the emerging topics and show their process of evolution. Finally, we conduct an empirical analysis in the field of information science, and the empirical analysis has proven that this identification method is effective and reliable. The identification method based on a media comparison index showed good identification ability and timeliness advantage. We also found that a satisfactory result can be achieved to some extent when using altmetrics indicators that are characterized by high level of coverage and prevalence, such as tweets or posts. 2022 Vol. 41 (9): 930-944 [Abstract] ( 202 ) HTML (1 KB)  PDF (2092 KB)  ( 389 )

945 Network Analysis of Inter-Division in Funding Projects: Case Studies of AI Field in NSF Data Hot! Yang Jie, Wang Yuefen, Chen Bikun, Hui Guangping DOI: 10.3772/j.issn.1000-0135.2022.09.005 This study aims to explore the funding direction, influence, characters, and evolution of a crossing study from the perspective of division by analyzing the research fusion and trends of funding projects. A co-word network analysis is combined with interdisciplinary research methods to construct an analysis framework and a measurement principle with an aim to discover the development of the inner-division knowledge and the inter-division research trends from the dimensions of the inner-division compactness of co-word networks and the intersection of inter-division knowledge. The results show that, over time, as per the National Science Foundation (NSF) data, both the number of projects and the diversity of topics in the different divisions of the artificial intelligence (AI) field are increasing. Further, from the two dimensions, the degree of inner-division compactness keeps decreasing while the intersection of inter-division knowledge intensifies, and there exists an obvious polarization between them. Moreover, the intersecting content of NSF’s different divisions is concentrated in the topics with the same or similar theoretical methods, which indicates that it is easier to realize cross-integration for similar knowledge, and there exists some relationship between the inner-division compactness of co-word networks and the crossing and integration of inter-division knowledge. 2022 Vol. 41 (9): 945-955 [Abstract] ( 244 ) HTML (1 KB)  PDF (2408 KB)  ( 578 )

956 Frontier Influence of the Inter-Directorate in Project Planning: Case Studies of AI Field in NSF Data Hot! Fan Lipeng, Wang Yuefen, Cen Yonghua, Yang Jie DOI: 10.3772/j.issn.1000-0135.2022.09.006 The purpose of this study is to calculate and analyze the inter-directorate degree in project planning; it further identifies the front of project planning for exploring the inter-directorate influence and the frontier distribution of project funding while considering the balance of researchers taking part in programs with different directorates and how many directorates the program involves. First, we use the Rao-Stirling diversity indicator to calculate the inter-directorate degree of the programs before dividing them into high-crossing, medium-crossing, low-crossing, and non-crossing types. According to the funding trend and intensity of the program, we then construct the indicators of frontier identification and divide the programs into cutting-edge, hot-spot, potential, and decline types with different inter-directorate degrees. Finally, we analyze the inter-directorate influence on the distribution of frontier programs and elaborate more on the cutting-edge programs in high-crossing types. The research results show that, for National Science Foundation (NSF) data in the artificial intelligence (AI) field, the number of crossing programs was approximately equal to that of non-crossing programs while the average funding of cross-type programs was much higher than that of non-crossing programs. From the perspective of the distribution in frontier programs, the non-crossing programs were generally the potential type, while high-crossing and low-crossing types were more likely to be cutting-edge programs. The funding trend was higher for high-crossing type than that for other crossing types. Moreover, it was observed that the fronts of high-crossing programs tend to focus on neural and cognitive systems, natural and human systems, and information intelligence systems. 2022 Vol. 41 (9): 956-966 [Abstract] ( 262 ) HTML (1 KB)  PDF (1490 KB)  ( 138 )

967 Topic Mining and Dynamic Evolution Analysis of Funding Projects: Case Studies of AI Field in NSF Data Hot! Jin Jialin, Wang Yuefen, Ba Zhichao, Cen Yonghua DOI: 10.3772/j.issn.1000-0135.2022.09.007 This study aims to construct an analysis process of topic mining and dynamic evolution for funding projects data. By modeling and mining the relationship between funding projects and the directorate using title and abstract metadata, this proposed process can locate the characteristics of topics, the scope and focus of the directorate, the developing direction, and the evolution of a certain funding field from the perspective of funding project content. Firstly, keywords are extracted from the title and abstract of the funding projects using the rapid automatic keyword extraction (RAKE) algorithm, and the core keywords are obtained through the term segmentation method. Subsequently, the word vector is modeled with the core keywords using the Google word2vec deep learning tool, and the word vector is clustered to mine the topics through the k-means algorithm. Finally, the distribution of topics is described and the similarity between the topics is calculated using the word mover's distance (WMD) algorithm for analyzing the evolutionary trend and the primary evolutionary path of the topics. Using artificial intelligence (AI) in the National Science Foundation (NSF) data, it is discovered that the proposed process can recognize the topics within the AI field and the specific focus of the different directorates. Moreover, through the proposed process, it is observed that the evolution of these topics presents a complex situation of a large number of division and integration, a clear evolutionary path, a prominent focus. and a key evolutionary path through the evolutionary intensity of topics, which indicate that this process can reveal the funding direction for integrating and promoting the related technology in certain fields and can provide strong support for academic research and government planning. 2022 Vol. 41 (9): 967-979 [Abstract] ( 258 ) HTML (1 KB)  PDF (4871 KB)  ( 159 )

980 Review of Disruptive Technologies Identification Method Based on Deductive and Inductive Logic Perspectives Hot! Zhou Bo, Leng Fuhai DOI: 10.3772/j.issn.1000-0135.2022.09.008 With the increasingly fierce competition and highly complex development of technologies around the world, the disruptive technologies identification method is receiving increasing attention from governments and the enterprises. In this study, disruptive technologies identification methods are widely analyzed. Two different perspectives, deductive and inductive logic, were identified. The identification methods were classified and reviewed based on the two perspectives, and the two perspectives were compared from four aspects, which are the research premises, research data, preciseness of research conclusions, and research processes. The advantages and disadvantages of the identification methods based on the two perspectives have been summarized, and finally, this paper presents a preliminary prospect for further research on the identification methods of disruptive technologies based on these two perspectives. This paper aims to provide a reference for the research of disruptive technologies identification methods in the future by comparing the research of disruptive technologies identification methods based on the two perspectives. 2022 Vol. 41 (9): 980-990 [Abstract] ( 314 ) HTML (1 KB)  PDF (849 KB)  ( 411 )

991 Review on Identifying the Semantics of Scientific Literature Content Hot! Huang Hong, Chen Chong, Zhang Jingying DOI: 10.3772/j.issn.1000-0135.2022.09.009 Identifying the semantics of the textual content of scientific literature can shed light on the research elements of scientific literature. This task is a kind of fine-grained text mining, and is essential for knowledge acquisition and utilization. This article reviews recent research studies on identification of semantics of scientific literature content; it is expected that such a review would provide comprehensive reference for subsequent studies. This study begins by summarizing the existing semantic annotation models of literature content, and then it discusses the research track of semantic identification of literature content based on different granularities (i.e. chapters, sentences and terms), illustrates the typical applications, highlights the existing problems, and suggests future research directions. The study seeks answers to five questions: (1) Which semantic types of scientific literature content are under focus? (2) What granularity of text units should be selected for semantic identification? (3) What kind of identification approaches are available? (4) How to evaluate the identification results? (5) What are the typical applications of semantic identification? Future improvement on this line of research includes proposing uniform standards on semantic types, increasing the available training data sets and focusing on multiple semantic types and their relations, and improving existing methods. It is important to continue making many efforts to find more solutions through future studies. 2022 Vol. 41 (9): 991-1002 [Abstract] ( 496 ) HTML (1 KB)  PDF (754 KB)  ( 319 )