[1] 索传军,盖双双.知识元的内涵、结构与描述模型研究[J].中国图书馆学报, 2018, 44(4):54-72.(SUO C J, GAI S S. The connotation, structure and description model of knowledge unit[J]. Journal of library science in China, 2018, 44(4):54-72.)
[2] 董坤,许海云,崔斌.知识流动研究述评[J].情报学报, 2020, 39(10):1120-1132.(DONG K, XU H Y, CUI B. A review of knowledge flow research[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(10):1120-1132.)
[3] 索传军,戎军涛.知识元理论研究述评[J].图书情报工作, 2021, 65(11):133-142.(SUO C J, RONG J T. Review on the theory of knowledge element[J]. Library and information service, 2021, 65(11):133-142.)
[4] TEECE D J. Technology transfer by multinational firms:the resource cost of transferring technological know-how[J]. The economic journal, 1977, 87(346):242-261.
[5] 朱雪春,彭灿,罗建强,等.基于二元语义多属性决策的实践社群知识流动绩效评价研究[J].科学学与科学技术管理, 2017, 38(5):101-116.(ZHU X C, PENG C, LUO J Q, et al. The research on knowledge flow performance evaluation for communities of practice based on two-tuple linguistic multiple decision method[J]. Science of science and management of S.&T., 2017, 38(5):101-116.)
[6] 吴玉浩,姜红,刘文韬.基于知识流动视角的"标准化+知识"战略协同机制研究[J].情报杂志, 2018, 37(8):180-185.(WU Y H, JIANG H, LIU W T. Research on strategic coordination mechanism of "standardization+knowledge" from the knowledge flow perspective[J]. Journal of intelligence, 2018, 37(8):180-185.)
[7] 徐晓艺,杨立英.基于合著论文的学科知识流动网络的特征分析——以"药物化学"学科为例[J].图书情报工作, 2015, 59(1):89-98.(XU X Y, YANG L Y. Analysis of disciplinary knowledge flows network based on coauthored papers:a case of medicinal chemistry discipline[J]. Library and information service, 2015, 59(1):89-98.)
[8] 杨瑞仙,郭孟含.学术虚拟社区核心用户知识流动特征研究[J].现代情报, 2022, 42(8):50-60.(YANG R X, GUO M H. Research on the knowledge flow characteristics of core users in academic virtual community[J]. Journal of modern information, 2022, 42(8):50-60.)
[9] HALL D, JURAFSKY D, MANNING C D. Studying the history of ideas using topic models[C]//Proceedings of the 2008 conference on empirical methods in natural language processing. Honolulu:Association for Computational Linguistics, 2008:363-371.
[10] TSAY M. Knowledge flow out of the domain of information science:a bibliometric and citation analysis study[J]. Scientometrics, 2015, 102(1):487-502.
[11] KHAKIMOVA A, YANG X, ZOLOTAREV O, et al. Tracking knowledge evolution based on the terminology dynamics in 4P-medicine[J]. International journal of environmental research and public health. 2020, 17(20):7444.
[12] 焦红,李秀霞.基于研究主题的学科领域知识演化路径识别——以图书情报领域粗糙集为例[J].情报理论与实践, 2019, 42(3):101-106.(JIAO H, LI X X. Identification of the knowledge evolution path in disciplinary field based on research topic:taking the rough set of information science and library science as an example[J]. Information studies:theory&application, 2019, 42(3):101-106.)
[13] YAN E. Disciplinary knowledge production and diffusion in science[J]. Journal of the American Society for Information Science and Technology, 2016, 67(9):2223-2245.
[14] DING C G, HUNG W, LEE M, et al. Exploring paper characteristics that facilitate the knowledge flow from science to technology[J]. Journal of informetrics, 2017, 11(1):244-256.
[15] 胡吉明,陈果.基于动态LDA主题模型的内容主题挖掘与演化[J].图书情报工作, 2014, 58(2):138-142.(HU J M, CHEN G. Mining and evolution of content topics based on dynamic LDA[J]. Library and information service, 2014, 58(2):138-142.)
[16] 颜端武,苏琼,张馨月.基于时序主题关联演化的科学领域前沿探测研究[J].情报理论与实践, 2019, 42(7):144-150.(DUAN Y W, SU Q, ZHANG X Y. Research on frontier detection in scientific field based on sequential topic association evolution[J]. Information studies:theory&application, 2019, 42(7):144-150.)
[17] LI G, JIANG Z H, LI X Y. A text mining-based approach for modelling technical knowledge evolution in patents[J]. International journal of technology, policy and management, 2020, 20(4):318-339.
[18] MORRIS S A, YEN G, ZHENG W, et al. Time line visualization of research fronts[J]. Journal of the Association for Information Science&Technology, 2014, 54(5):413-422.
[19] 张瑞,何禄鑫,杨艳妮.考虑语义距离的领域科学知识主题关联与演化研究[J].情报杂志, 2022, 41(10):121-129.(ZHANG R, HE L X, YANG Y N. Topic association and evolution research of domain scientific knowledge considering semantic distance[J]. Journal of intelligence, 2022, 41(10):121-129.)
[20] KIM H, PARK Y. Structural effects of R&D collaboration network on knowledge diffusion performance[J]. Expert systems with applications, 2009, 36(5):8986-8992.
[21] PHAM M C, KLAMMA R, JARKE M. Development of computer science disciplines-a social network analysis approach[J]. Social network analysis and mining, 2011, 1(4):321-340.
[22] GONG J X, VIKRANT S, KONG Q X, et al. Visualizing knowledge evolution trends and research hotspots of personal health data research:bibliometric analysis[J]. JMIR medical informatics, 2021, 9(11):e31142.
[23] 吴小兰,章成志.学术社交媒体视角下学科知识流动规律研究——以科学网为例[J].数据分析与知识发现, 2019, 3(4):107-116.(WU X L, ZHANG C Z. Analysis of knowledge flow based on academic social networks:a case study of sciencenet.cn[J]. Data analysis and knowledge discovery, 2019, 3(4):107-116.)
[24] 叶光辉,彭泽,毕崇武,等."数字人文"领域科研协作知识交流中的学科交叉与地域交叉测度分析[J].情报学报, 2022, 41(5):512-524.(YE G H, PENG Z, BI C W, et al. Influence of subject and region intersection in the knowledge exchange of scientific academic collaboration in digital humanities[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(5):512-524.)
[25] 王佳敏,陆伟,程齐凯,等.基于细粒度关键词引用网络的领域知识多维分析[J].情报学报, 2022, 41(7):733-744.(WANG J M, LU W, CHENG Q K, et al. Multi-dimensional analysis of domain knowledge based on a fine-grained keyword citation network[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(7):733-744.)
[26] HOWISON J, BULLARD J. Software in the scientific literature:problems with seeing, finding, and using software mentioned in the biology literature[J]. Journal of the Association for Information Science&Technology, 2015, 67(9):2137-2155.
[27] SENEVIRATNE M, FERNANDO K, KARUNARATNE D, et al. Markov logic for statistical relation extraction[J]. International journal of advanced and applied Sciences, 2022, 7(10):1-11.
[28] 朱玲,朱彦,杨峰.基于中医疾病相关语义关系的正则表达式及知识抽取研究[J].世界科学技术-中医药现代化, 2016, 18(8):1241-1250.(ZHU L, ZHU Y, YANG F. Knowledge extraction research for semantic expression of diseases in Chinese medicine[J]. Modernization of traditional Chinese medicine and materia medica-world science and technology, 2016, 18(8):1241-1250.)
[29] 丁君军,郑彦宁,化柏林.基于规则的学术概念属性抽取[J].情报理论与实践, 2011, 34(12):10-14.(LI J J, ZHENG Y N, HUA B L. Attribute exception of academic conception based on rules[J]. Information studies:theory&application, 2011, 34(12):10-14.)
[30] KAYVAN B, YASMEEN Z B,GIORGIO A. Assisted neuroscience knowledge extraction via machine learning applied to neural reconstruction metadata on NeuroMorpho.org[J]. Brain informatics, 2022, 9(1):26-26.
[31] LIAKATA M, SAHA S, DOBNIK S, et al. Automatic recognition of conceptualization zones in scientific articles and two life science applications[J]. Bioinformatics, 2012, 28(7):991-1000.
[32] ZENG D, LIU K, LAI S, et al. Relation classification via convolutional deep neural network[C]//The 25th international conference on computational linguistics. Dublin:Proceedings of COLING 2014, 2014:2335-2344.
[33] NIKOLA M, WOLFGANG T. Comparison of biomedical relationship extraction methods and models for knowledge graph creation[J]. Journal of Web semantics, 2023, 75:100756.
[34] 徐小亮,赵英.基于BiLSTM和ResCNN的实体关系抽取方法[J].计算机与现代化, 2022(1):10-16.(XU X L, ZHAO Y. Relationship extraction method based on BiLSTM and ResCNN[J]. Computer and modernization, 2022(1):10-16.)
[35] LEE K, JUNG H, SONG M, et al. Subject-method topic network analysis in communication studies[J]. Scientometrics, 2016, 109(3):1761-1787.
[36] JENSEN S, LIU X, YU Y, et al. Generation of topic evolution trees from heterogeneous bibliographic networks[J]. Journal of informetrics, 2016, 10(2):606-621.
[37] YAO R J, HOU L L, YE Y C, et al. Method and dataset mining in scientific papers[C]//2019 IEEE international conference on big data. Los Angeles:IEEE, 2019:6260-6262.
[38] MAO J, LIANG Z T, CAO Y J, et al. Quantifying cross-disciplinary knowledge flow from the perspective of content:introducing an approach based on knowledge memes[J]. Journal of informetrics, 2020, 14(4):101092.
[39] 陈果,许天祥.基于主动学习的科技论文句子功能识别研究[J].数据分析与知识发现, 2019, 3(8):53-61.(CHEN G, XU T X. Sentence function recognition based on active learning[J]. Data analysis and knowledge discovery, 2019, 3(8):53-61.)
[40] 郑梦悦,秦春秀,马续补.面向中文科技文献非结构化摘要的知识元表示与抽取研究——基于知识元本体理论[J].情报理论与实践, 2020, 43(2):157-163.(ZHENG M Y, QIN C X, MA X B. Research on knowledge unit representation and extraction for unstructured abstracts of Chinese scientific and technical literature:ontology theory based on knowledge unit[J]. Information studies:theory&application, 2020, 43(2):157-163.)
[41] 秦成磊,章成志.基于层次注意力网络模型的学术文本结构功能识别[J].数据分析与知识发现, 2020, 4(11):26-42.(QIN C L, ZHANG C Z. Recognizing structure functions of academic articles with hierarchical attention network[J]. Data analysis and knowledge discovery, 2020, 4(11):26-42.)
[42] TENEVA N, CHENG W. Salience rank:efficient keyphrase extraction with topic modeling[C]//Proceedings of the 55th annual meeting of the Association for Computational Linguistics. Vancouver:Association for Computational Linguistics, 2017:530-535.
[43] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013.
[44] 李金阳.图数据库在图书馆的应用研究[J].图书馆, 2020, 314(11):109-115.(LI J Y. Research on the application of graph database in library[J]. Library, 2020, 314(11):109-115.)
