核心-边缘结构视角下产业界和学术界知识成熟度差异研究

doi:10.3772/j.issn.1000-0135.2026.05.009

情报学报

2026, Vol. 45

Issue (5): 740-757 DOI: 10.3772/j.issn.1000-0135.2026.05.009

情报技术与应用

本期目录 | 过刊浏览 | 高级检索

核心-边缘结构视角下产业界和学术界知识成熟度差异研究

赵洪烨¹, 赵毅², 章成志¹

1.南京理工大学经济管理学院信息管理系，南京 210094
2.安徽大学管理学院管理科学与工程系，合肥 230601

Differences in Knowledge Maturity Between Industry and Academia from the Perspective of Core-Periphery Structure

Zhao Hongye¹, Zhao Yi², Zhang Chengzhi¹

1.Department of Information Management, School of Economics & Management, Nanjing University of Science & Technology, Nanjing 210094
2.Department of Management Science and Engineering, School of Management, Anhui University, Hefei 230601

摘要
图/表
参考文献
相关文章 (0)

全文: PDF (2740 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要知识成熟度对学术创新与产业转化至关重要。准确识别产学双方在知识成熟度上的差异，可为创新资源配置及人才发展提供依据。然而，现有研究通常以整篇文献为知识的代理，通过参考文献平均发表年份估算知识成熟度，忽视了文献中蕴含的细粒度知识及其异质性的成熟轨迹，限制了知识成熟度测量的准确性。为此，本文以细粒度实体表征知识，提出基于核心-边缘结构的知识成熟度评估框架，以人工智能领域1845637篇论文作为研究对象展开分析。首先，利用计算机科学本体分类器抽取论文中的实体，将其构建为动态知识网络节点；其次，通过核心-边缘划分算法识别实体的结构位置，追踪其从边缘向核心演化的路径，以实体首次进入核心与焦点论文发表的时间间隔量化其成熟度；最后，结合多元回归分析产学界在知识成熟度使用上的差异。研究发现，实体从网络边缘到核心的演化路径可有效表征成熟度，该测度与论文新颖性、颠覆性显著相关。在此基础上，证实了学术界及产学合作论文的知识成熟度显著低于产业界论文。本文突破了以往基于文献整体的分析范式，从细粒度实体层面揭示了产学知识成熟度差异。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	赵洪烨
	赵毅
	章成志

关键词 ：核心-边缘结构, 计算机科学本体, 知识成熟度测度, 产学知识成熟度差异

收稿日期: 2025-08-18

基金资助:国家自然科学基金项目“基于学术文献全文内容的细粒度算法实体抽取与评估研究”（72074113）；江苏省研究生科研基金项目“基于知识实体的学术界与工业界知识演化模式差异研究”（KYCX25_0820）。

作者简介: 赵洪烨，2000年生，博士研究生，研究方向为知识实体挖掘与评价；赵毅，1993年生，博士，讲师，研究方向为文本挖掘与科学计量；章成志，通信作者，1977年生，博士，教授，博士生导师，研究方向为信息组织、信息检索、文本挖掘与自然语言处理，E-mail：zhangcz@njust.edu.cn；

引用本文:

赵洪烨, 赵毅, 章成志. 核心-边缘结构视角下产业界和学术界知识成熟度差异研究[J]. 情报学报, 2026, 45(5): 740-757.
Zhao Hongye, Zhao Yi, Zhang Chengzhi. Differences in Knowledge Maturity Between Industry and Academia from the Perspective of Core-Periphery Structure. 情报学报, 2026, 45(5): 740-757.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2026.05.009 或 https://qbxb.istic.ac.cn/CN/Y2026/V45/I5/740

1 赵丹晓, 龚红, 董姗. 知识成熟度对关键核心技术创新的影响研究[J]. 管理学报, 2025, 22(12): 2271-2279.
2 Corrocher N, Lenzi C. Exploring the sources of knowledge diversity in founding teams and its impact on new firms’ innovation[J]. Journal of Evolutionary Economics, 2022, 32(4): 1091-1118.
3 Lin K X, Hu B B, Li Z X, et al. Knowledge substitutability and complementarity in scientific collaboration[J]. Journal of Informetrics, 2025, 19(1): 101601.
4 Fleming L, Sorenson O. Technology as a complex adaptive system: evidence from patent data[J]. Research Policy, 2001, 30(7): 1019-1039.
5 Capaldo A, Lavie D, Messeni Petruzzelli A. Knowledge maturity and the scientific value of innovations: the roles of knowledge distance and adoption[J]. Journal of Management, 2017, 43(2): 503-533.
6 Kuhn T S. The essential tension: tradition and innovation in scientific research[C]// Proceedings of the Third University of Utah Conference on the Identification of Creative Scientific Talent. Salt Lake City: University of Utah Press, 1959: 162-174.
7 Bourdieu P. The specificity of the scientific field and the social conditions of the progress of reason[J]. Social Science Information, 1975, 14(6): 19-47.
8 Foster J G, Rzhetsky A, Evans J A. Tradition and innovation in scientists’ research strategies[J]. American Sociological Review, 2015, 80(5): 875-908.
9 Merton R K. Priorities in scientific discovery: a chapter in the sociology of science[J]. American Sociological Review, 1957, 22(6): 635-659.
10 Mueller J S, Melwani S, Goncalo J A. The bias against creativity: why people desire but reject creative ideas[J]. Psychological Science, 2012, 23(1): 13-17.
11 Wang J, Veugelers R, Stephan P. Bias against novelty in science: a cautionary tale for users of bibliometric indicators[J]. Research Policy, 2017, 46(8): 1416-1436.
12 Liang Z T, Mao J, Li G. Bias against scientific novelty: a prepublication perspective[J]. Journal of the Association for Information Science and Technology, 2023, 74(1): 99-114.
13 Chen P Z. Recombinant reuse or recombinant creation? The impact of knowledge recombination strategies on new product performance[J]. Technology Analysis & Strategic Management, 2023, 35(10): 1263-1277.
14 马荣康, 王艺棠. 知识组合多样性、新颖性与突破性发明形成[J]. 科学学研究, 2020, 38(2): 313-322.
15 Lai X P, Nie L B. Maturity of knowledge inputs and the breakthrough of key core technology[J]. Scientometrics, 2024, 129(11): 6551-6570.
16 Clarysse B, Andries P, Boone S, et al. Institutional logics and founders’ identity orientation: why academic entrepreneurs aspire lower venture growth[J]. Research Policy, 2023, 52(3): 104713.
17 Merton R K. The normative structure of science[M]// The Sociology of Science: Theoretical and Empirical Investigations. Chicago: University of Chicago Press, 1973: 267-278.
18 Liang L Z, Zhuang H, Zou J, et al. The complementary contributions of academia and industry to AI research[PP/OL]. V2. arXiv (2024-09-18). https://arxiv.org/pdf/2401.10268.
19 Ahmed N, Wahed M, Thompson N C. The growing influence of industry in AI research[J]. Science, 2023, 379(6635): 884-886.
20 F?rber M, Tampakis L. Analyzing the impact of companies on AI research based on publications[J]. Scientometrics, 2024, 129(1): 31-63.
21 Jee S J, Sohn S Y. Firms’ influence on the evolution of published knowledge when a science-related technology emerges: the case of artificial intelligence[J]. Journal of Evolutionary Economics, 2023, 33(1): 209-247.
22 Dwivedi Y K, Hughes L, Ismagilova E, et al. Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy[J]. International Journal of Information Management, 2021, 57: 101994.
23 Schumpeter J A. A theoretical, historical and statistical analysis of the capitalist process[M]. New York: McGraw-Hill, 1939.
24 Fleming L. Recombinant uncertainty in technological search[J]. Management Science, 2001, 47(1): 117-132.
25 March J G. Exploration and exploitation in organizational learning[J]. Organization Science, 1991, 2(1): 71-87.
26 Arts S, Fleming L. Paradise of novelty—or loss of human capital? Exploring new fields and inventive output[J]. Organization Science, 2018, 29(6): 1074-1092.
27 Fleming L, Mingo S, Chen D. Collaborative brokerage, generative creativity, and creative success[J]. Administrative Science Quarterly, 2007, 52(3): 443-475.
28 Uzzi B, Mukherjee S, Stringer M, et al. Atypical combinations and scientific impact[J]. Science, 2013, 342(6157): 468-472.
29 Verhoeven D, Bakker J, Veugelers R. Measuring technological novelty with patent-based indicators[J]. Research Policy, 2016, 45(3): 707-723.
30 Wu L F, Wang D S, Evans J A. Large teams develop and small teams disrupt science and technology[J]. Nature, 2019, 566(7744): 378-382.
31 Funk R J, Owen-Smith J. A dynamic network measure of technological change[J]. Management Science, 2017, 63(3): 791-817.
32 王春, 冷伏海. 基于应用基础研究的新兴技术方向成熟度评估方法研究[J]. 情报学报, 2025, 44(6): 660-674.
33 赵丹晓, 龚红. 知识成熟度、桥接科学家与衍生技术创新[J]. 南开管理评论, 2025, 28(10): 40-51.
34 Salatino A A, Osborne F, Thanapalasingam T, et al. The CSO classifier: ontology-driven detection of research topics in scholarly articles[C]// Proceedings of the 23rd International Conference on Theory and Practice of Digital Libraries. Cham: Springer, 2019: 296-311.
35 Salatino A, Osborne F, Motta E. CSO classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics[J]. International Journal on Digital Libraries, 2022, 23(1): 91-110.
36 Messeni Petruzzelli A, Ardito L, Savino T. Maturity of knowledge inputs and innovation value: the moderating effect of firm age and size[J]. Journal of Business Research, 2018, 86: 190-201.
37 Arts S, Hou J N, Gomez J C. Natural language processing to identify the creation and impact of new technologies in patent text: code, data, and new measures[J]. Research Policy, 2021, 50(2): 104144.
38 张鹤翔, 孙震, 唐苗. 基于多维知识元的科学—技术关联主题识别及发展态势测度研究——以人工智能领域为例[J]. 现代情报, 2026, 46(2): 61-76.
39 黄颖, 张慧, 叶冬梅, 等. 基于Gartner曲线的颠覆性技术生命周期模型构建研究——以新一代人工智能技术为例[J]. 现代情报, 2025, 45(8): 70-84.
40 关鹏, 王曰芬. 基于LDA主题模型和生命周期理论的科学文献主题挖掘[J]. 情报学报, 2015, 34(3): 286-299.
41 Evans J A. Industry induces academic science to know less about more[J]. American Journal of Sociology, 2010, 116(2): 389-452.
42 Wang Y Z, Zhang C Z, Song M, et al. Exploring academic influence of algorithms by co-occurrence network based on full-text of academic papers[J]. Aslib Journal of Information Management, 2025, 77(4): 651-680.
43 栾心晨, 黄永源, 朱晟君, 等. 被低估的边缘: 边缘区域创新研究综述[J]. 经济地理, 2024, 44(11): 1-12.
44 Kuhn T S. The structure of scientific revolutions[M]. Chicago: University of Chicago Press, 1962.
45 Boland R J, Tenkasi R V. Perspective making and perspective taking in communities of knowing[J]. Organization Science, 1995, 6(4): 350-372.
46 Jeppesen L B, Lakhani K R. Marginality and problem-solving effectiveness in broadcast search[J]. Organization Science, 2010, 21(5): 1016-1033.
47 Safadi H, Johnson S L, Faraj S. Who contributes knowledge? Core-periphery tension in online innovation communities[J]. Organization Science, 2021, 32(3): 752-775.
48 érdi P, Makovi K, Somogyvári Z, et al. Prediction of emerging technologies based on analysis of the US patent citation network[J]. Scientometrics, 2013, 95(1): 225-242.
49 Raman R, Pattnaik D, Hughes L, et al. Unveiling the dynamics of AI applications: a review of reviews using scientometrics and BERTopic modeling[J]. Journal of Innovation & Knowledge, 2024, 9(3): 100517.
50 Song B W, Luan C J, Liang D N. Identification of emerging technology topics (ETTs) using BERT-based model and sematic analysis: a perspective of multiple-field characteristics of patented inventions (MFCOPIs)[J]. Scientometrics, 2023, 128(11): 5883-5904.
51 Gl?nzel W, Thijs B. Using hybrid methods and ‘core documents’ for the representation of clusters and topics: the astronomy dataset[J]. Scientometrics, 2017, 111(2): 1071-1087.
52 Wei W J, Liu H X, Sun Z L. Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework[J]. Scientometrics, 2022, 127(8): 4315-4333.
53 Haeussler C. Information-sharing in academia and the industry: a comparative study[J]. Research Policy, 2011, 40(1): 105-122.
54 Martinez-Senra A I, Quintas M A, Sartal A, et al. How can firms’ basic research turn into product innovation? The role of absorptive capacity and industry appropriability[J]. IEEE Transactions on Engineering Management, 2015, 62(2): 205-216.
55 Chen Z L, Zhang C Z, Zhang H, et al. Exploring the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entities[J]. The Electronic Library, 2024, 42(6): 905-930.
56 Sauermann H, Stephan P E. Twins or strangers? Differences and similarities between industrial and academic science[R]. Cambridge: National Bureau of Economic Research, 2010: Working Paper 16113.
57 Gans J S, Murray F E, Stern S. Contracting over the disclosure of scientific knowledge: intellectual property and academic publication[J]. Research Policy, 2017, 46(4): 820-835.
58 Al-Khalifa H S, AlOmar T, AlOlyyan G. Natural language processing patents landscape analysis[J]. Data, 2024, 9(4): 52.
59 赵毅, 章成志, 习海旭. 影响不同子领域国际合作的距离因素相同吗?——来自计算机科学学科的证据[J]. 情报学报, 2023, 42(12): 1458-1476.
60 Zhang L, Cao Z, Shang Y Y, et al. Missing institutions in OpenAlex: possible reasons, implications, and solutions[J]. Scientometrics, 2024, 129(10): 5869-5891.
61 Xu H M, Bu Y, Liu M J, et al. Team power dynamics and team impact: new perspectives on scientific collaboration using career age as a proxy for team power[J]. Journal of the Association for Information Science and Technology, 2022, 73(10): 1489-1505.
62 Dessí D, Osborne F, Buscaldi D, et al. CS-KG 2.0: a large-scale knowledge graph of computer science[J]. Scientific Data, 2025, 12: 964.
63 Meng K, Ba Z C, Ma Y X, et al. A network coupling approach to detecting hierarchical linkages between science and technology[J]. Journal of the Association for Information Science and Technology, 2024, 75(2): 167-187.
64 Kedrick K, Levitskaya E, Funk R J. Conceptual structure and the growth of scientific knowledge[J]. Nature Human Behaviour, 2024, 8(10): 1915-1923.
65 Chen C M. The growth of scientific knowledge[M]// Mapping Scientific Frontiers: The Quest for Knowledge Visualization. London: Springer, 2003: 1-38.
66 Borgatti S P, Everett M G. Models of core/periphery structures[J]. Social Networks, 2000, 21(4): 375-395.
67 Kojaku S, Masuda N. Finding multiple core-periphery pairs in networks[J]. Physical Review E, 2017, 96(5): 052313.
68 Kojaku S, Masuda N. Core-periphery structure requires something else in the network[J]. New Journal of Physics, 2018, 20(4): 043012.
69 Tun? B, Verma R. Unifying inference of meso-scale structures in networks[J]. PLoS One, 2015, 10(11): e0143133.
70 Milo R, Shen-Orr S, Itzkovitz S, et al. Network motifs: simple building blocks of complex networks[J]. Science, 2002, 298(5594): 824-827.
71 van Raan A F J. Sleeping beauties in science[J]. Scientometrics, 2004, 59(3): 467-472.
72 Dosi G. Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change[J]. Research Policy, 1982, 11(3): 147-162.
73 邱均平. 信息计量学(三) 第三讲文献信息老化规律与应用[J]. 情报理论与实践, 2000, 23(3): 237-240, 192.
74 邱均平. 文献计量学[M]. 2版. 北京: 科学出版社, 2019.
75 Mukherjee S, Romero D M, Jones B, et al. The nearly universal link between the age of past knowledge and tomorrow’s breakthroughs in science and technology: the hotspot[J]. Science Advances, 2017, 3(4): e1601315.
76 Lin Z H, Yin Y A, Liu L, et al. SciSciNet: a large-scale open data lake for the science of science research[J]. Scientific Data, 2023, 10: Article No.315.
77 Wu K Y, Sun J J, Wang J J, et al. How does science convergence influence technology convergence? Different impacts of science-push and technology-pull[J]. Technological Forecasting and Social Change, 2025, 215: 124114.
78 Larivière V, Gingras Y, Sugimoto C R, et al. Team size matters: collaboration and scientific impact since 1900[J]. Journal of the Association for Information Science and Technology, 2015, 66(7): 1323-1332.
79 Wagner C S, Whetsell T A, Mukherjee S. International research collaboration: novelty, conventionality, and atypicality in knowledge recombination[J]. Research Policy, 2019, 48(5): 1260-1270.
80 Bercovitz J, Feldman M. The mechanisms of collaboration in inventive teams: composition, social networks, and geography[J]. Research Policy, 2011, 40(1): 81-93.
81 Baraba?si A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439): 509-512.
82 Rafols I, Meyer M. Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience[J]. Scientometrics, 2010, 82(2): 263-287.
83 Bush V. Science, the endless frontier[M]. Princeton: Princeton University Press, 1945.
84 聂力兵, 龚红, 赖秀萍. 唤醒“沉睡专利”: 知识重组时滞、重组频率与关键核心技术创新[J]. 南开管理评论, 2024, 27(8): 86-97, 160.
85 Higashide N, Zhang Y, Asatani K, et al. Quantifying advances from basic research to applied research in material science[J]. Technovation, 2024, 135: 103050.
86 Zuckerman H. Scientific elite: Nobel laureates in the United States[M]. New York: The Free Press, 1977.
87 Xie X, Mao J, Li J. How does Nobel Prize awarding shift the research topics of Nobelists’ coauthors and non-coauthors?[J]. Journal of Informetrics, 2025, 19(1): 101602. 责任编辑冯家琪）