[1] AITTOLA M, RYHNEN T, OJALA T. SmartLibrary-locationaware mobile library service[C]//CHITTARO L. Human computer interaction with mobile devices and services. Berlin:Springer, 2003:411-416.
[2] 新华社.中华人民共和国国民经济和社会发展第十四个五年规划和2035年远景目标纲要[EB/OL].[2023-12-10]. https://www.gov.cn/xinwen/2021-03/13/content_5592681.htm. (Xinhua News Agency. Outline of the 14th five-year plan (2021-2025) for national economic and social development and vision 2035 of the People's Republic of China[EB/OL].[2023-12-10]. https://www.gov.cn/xinwen/2021-03/13/content_5592681.htm.)
[3] 王世伟.论智慧图书馆的三大特点[J].中国图书馆学报, 2012, 38(6):22-28.(WANG S W. On three main features of the smart library[J]. Journal of library science in China, 2012, 38(6):22-28.)
[4] 夏立新,白阳,李成龙.基于SoLoMo的智慧自助图书馆服务体系研究[J].图书情报工作, 2015, 59(4):32-36, 82.(XIA L X, BAI Y, LI C L. The research of the service system to the smart self-service library based on the SoLoMo[J]. Library and information service, 2015, 59(4):32-36, 82.)
[5] 王家玲.基于智慧要素视角的智慧图书馆构建[J].图书馆工作与研究, 2017(7):41-44, 49.(WANG J L. Construction of the smart library based on perspective of smart elements[J]. Library work and study, 2017(7):41-44, 49.)
[6] 魏大威,李志尧,刘晶晶,等.基于区块链技术的智慧图书馆数字资源管理研究[J].中国图书馆学报, 2022, 48(2):4-12.(WEI D W, LI Z Y, LIU J J, et al. Digital resource management of smart library based on blockchain technology[J]. Journal of library science in China, 2022, 48(2):4-12.)
[7] 刘炜,陈晨,张磊. 5G与智慧图书馆建设[J].中国图书馆学报, 2019, 45(5):42-50.(LIU W, CHEN C, ZHANG L. 5G and smart library construction[J]. Journal of library science in China, 2019, 45(5):42-50.)
[8] 田杰. 5G信息管理背景下智慧图书馆VR服务平台构建[J].情报科学, 2021, 39(5):124-129.(TIAN J. Construction of VR service platform of intelligent library under the background of 5G information management[J]. Information science, 2021, 39(5):124-129.)
[9] 顾佐佐,陈虹,李晓玥,等.智慧图书馆动态知识服务体系构建与平台设计[J].情报科学, 2020, 38(10):119-124.(GU Z Z, CHEN H, LI X Y, et al. Construction of smart library knowledge service system and its platform design[J]. Information science, 2020, 38(10):119-124.)
[10] 李强.新一代人工智能+5G技术环境下的智慧图书馆新生态[J].图书馆理论与实践, 2021(3):52-57.(LI Q. A new ecology of smart libraries under the environment of new generation artificial intelligence+5G technology[J]. Library theory and practice, 2021(3):52-57.)
[11] 陈观婷,张震,黄奇.元宇宙视域下的智慧图书馆:融合人的智慧与物的智能的服务生态[J].图书情报工作, 2023, 67(10):15-25.(CHEN G T, ZHANG Z, HUANG Q. the smart library from the perspective of metaverse:a service ecology integrating wisdom of humans and intelligence of things[J]. Library and information service, 2023, 67(10):15-25.)
[12] 赵杨,张雪,范圣悦. AIGC驱动的智慧图书馆转型:框架、路径与挑战[J].情报理论与实践, 2023, 46(7):9-16.(ZHAO Y, ZHANG X, FAN S Y. AIGC-driven intelligent library transformation:framework, pathways and challenges[J]. information studies:theory&application, 2023, 46(7):9-16.)
[13] 徐芳.智慧图书馆生成式人工智能应用场景及其法律问题[J/OL].情报资料工作, 1-10[2024-02-27]. http://kns.cnki.net/kcms/detail/11.1448.G3.20231225.1753.005.html. (XU F. Smart library generative artificial intelligence application scenario and its legal issues[J/OL]. Information and documentation services, 1-10[2024-02-27]. http://kns.cnki.net/kcms/detail/11.1448.G3.20231225.1753.005.html.)
[14] 陈涛,刘炜,单蓉蓉,等.知识图谱在数字人文中的应用研究[J].中国图书馆学报, 2019, 45(6):34-49.(CHEN T, LIU W, SHAN R R, et al. Application of knowledge graph in digital humanities[J]. Journal of library science in China, 2019, 45(6):34-49.)
[15] ZOU X. A survey on application of knowledge graph[J]. Journal of physics:conference series, 2020, 1487(1):012016.
[16] 陈博立,鲜国建,赵瑞雪,等.科技文献问答式智能检索总体设计与关键技术探析[J].中国图书馆学报, 2023, 49(3):92-106.(CHEN B L, XIAN G J, ZHAO R X, et al. Overall design and key technology of Q&A style intelligent retrieval for scientific and technical literature[J]. Journal of library science in China, 2023, 49(3):92-106.)
[17] MA J, ZHONG M, WEN J, et al. RecKGC:integrating recommendation with knowledge graph completion[C]//International conference on advanced data mining and applications. Berlin:Springer, 2019:250-265.
[18] 毛瑞彬,朱菁,李爱文,等.基于自然语言处理的产业链知识图谱构建[J].情报学报, 2022, 41(3):287-299.(MAO R B, ZHU J, LI A W, et al. Construction of knowledge graph of industry chain based on natural language processing[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(3):287-299.)
[19] 李纲,王施运,毛进,等.面向态势感知的国家安全事件图谱构建研究[J].情报学报, 2021, 40(11):1164-1175.(LI G, WANG S Y, MAO J, et al. Construction of national security event map and its application for situation awareness[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(11):1164-1175.)
[20] GUO Q, ZHUANG F, QIN C, et al. A survey on knowledge graph-based recommender systems[J]. Scientia sinica informationis, 2020, 50(7):937-956.
[21] 黄茜茜,杨建林.基于司法判决书的知识图谱构建与知识服务应用分析[J].情报科学, 2022, 40(2):133-140.(HUANG X X, YANG J L. Construction of knowledge graph and analysis of knowledge service application based on judicial decision documents[J]. Information science, 2022, 40(2):133-140.)
[22] FENGJUN S, CHUNFU R. An entity recognition model based on deep learning fusion of text feature[J]. Information processing and management, 2022, 59(2):102841.
[23] BOLLACKER K, COOK R, TUFTS P. Freebase:a shared database of structured general human knowledge[C]//Proceedings of the 22nd national conference on artificial intelligence. Menlo Park:AAAI, 2007:1962-1963.
[24] AUER S, BIZER C, KOBILAROV G, et al. DBpedia:a nucleus for a web of open data[C]//International semantic Web conference, Asian semantic web conference. Heidelberg:LNISA, 2007, 4825:722-735.
[25] CHEN P, LU Y, ZHENG V W, et al. KnowEdu:a system to construct knowledge graph for education[J]. IEEE access, 2018, 6:31553-31563.
[26] PAYAL C, KEXIN H, MARINKA Z. Building a knowledge graph to enable precision medicine[J]. Scientific data, 2023, 10(1):67.
[27] 钱玲飞,崔晓蕾.基于数据增强的领域知识图谱构建方法研究[J].现代情报, 2022, 42(3):31-39.(QIAN L F, CUI X L. Research on construction method of domain knowledge graph based on transfer learning[J]. Journal of modern information, 2022, 42(3):31-39.)
[28] ROSSANEZ A, REIS J. Generating knowledge graphs from scientific literature of degenerative diseases[C]//Proceedings of the 4th international workshop on semantics-powered data mining and analytics. Aukland:SEPDA, 2019:12-23.
[29] WANG C, MA X, CHEN J, et al. Information extraction and knowledge graph construction from geoscience literature[J]. Computers&geosciences, 2018, 112:112-120.
[30] 赵雪芹,李天娥,曾刚.基于Neo4j的万里茶道数字资源知识图谱构建研究[J].情报资料工作, 2022, 43(5):89-97.(ZHAO X Q, LI T E, ZENG G. Analysis of the tea road digital resource knowledge map construction based on neo4j[J]. Information and documentation services, 2022, 43(5):89-97.)
[31] ETZIONI O, CAFARELLA M, DOWNEY D, et al. Unsupervised named-entity extraction from the web:an experimental study[J]. Artificial intelligence, 2005, 165(1):91-134.
[32] ZHANG S, ELHADAD N. Unsupervised biomedical named entity recognition:experiments with clinical and biological texts[J]. Journal biomedical information, 2013, 46(6):1-29.
[33] HAN H, WANG J, WANG X. A relation-oriented model with global context information for joint extraction of overlapping relations and entities[J]. Frontiers in neurorobotics, 2022, 16:914705.
[34] GORMLEY M R, YU M, DREDZE M. Improved relation extraction with feature-rich compositional embedding models[C]//Proceeding of the 2015 conference on empirical methods in natural language processing. Stroudsburg:ACL, 2015:1774-1784.
[35] PANG Y, LIU J, ZHOU J, et al. A deep neural network model for joint entity and relation extraction[J]. IEEE access, 2019, 7:179143-179150.
[36] ZGA B, YZA B, YHA B. Joint entity and relation extraction model based on rich semantics[J]. Neuro computing, 2021, 429:132-140.
[37] COLLOBERT R, WESTON J, BOTTOU L, et al. Natural language processing (almost) from scratch[J]. Journal of machine learning research, 2011, 12(1):2493-2537.
[38] 翟羽佳,田静文,赵玥.基于BERT-BiLSTM-CRF模型的算法术语抽取与创新演化路径构建研究[J].情报科学, 2022, 40(4):71-78.(ZHAI Y J, TIAN J W, ZHAO Y. Algorithm term extraction and innovation evolution path construction based on BERT-BiLSTM-CRF model[J]. Information science, 2022, 40(4):71-78.)
[39] 吴俊,程垚,郝瀚,等.基于BERT嵌入BiLSTM-CRF模型的中文专业术语抽取研究[J].情报学报, 2020, 39(4):409-418.(WU J, CHENG Y, HAO H, et al. Automatic extraction of Chinese terminology based on BERT embedding and BiLSTMCRF model[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(4):409-418.)
[40] PETERS M, NEUMANN M, IYYER M, et al. Deep contextualized word representations[C]//Proceedings of the 2018 conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. New Orleans:ACL, 2018:2227-2237.
[41] DEVLIN J, CHANG M W, LEE K, et al. BERT:pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Minneapolis:NAACL-HLT, 2019(1):4171-4186.
[42] HU W, MA B, LI Z, et al. A cross-media deep relationship classification method using discrimination information[J]. Information processing&management, 2020, 57(6):102344.
[43] LI Q, LI L, WANG W, et al. A comprehensive exploration of semantic relation extraction via pre-trained CNNs[J]. Knowledge-based systems, 2020, 194:105488.
[44] SOCHER R, HUVAL B, MANNING C D, et al. Semantic compositionality through recursive matrix-vector spaces[C]//Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Jeju Island:ACL, 2012:1201-1211.
[45] WU S, HE Y. Enriching pre-trained language model with entity information for relation classification[C]//Proceedings of the 28th ACM international conference on information and knowledge management. New York:ACM, 2019:2361-2364.
[46] 任亮,杜薇薇,刘伟利.面向科技文献知识元的知识图谱构建研究[J].情报科学, 2022, 40(9):26-31.(REN L, DU W W, LIU W L. The construction of knowledge graph for knowledge elements of scientific literature[J]. Information science, 2022, 40(9):26-31.)
[47] 罗凌,杨志豪,宋雅文,等.基于笔画ELMo和多任务学习的中文电子病历命名实体识别研究[J].计算机学报, 2020, 43(10):1943-1957.(LUO L, YANG Z H, SONG Y W, et al. Chinese clinical named entity recognition based on stroke ELMo and multi-task learning[J]. Chinese journal of computers, 2020, 43(10):1943-1957.)
[48] HRIPCSAK G, ROTHSCHILD A S. Agreement, the f-measure, and reliability in information retrieval[J]. Journal of the American Medical Informatics Association, 2005, 12(3):296-298.
[49] 刘金岭.基于语义密度的文本聚类研究[J].计算机工程, 2010, 36(5):81-83.(LIU J L. Study on text clustering based on semantic density[J]. Computer engineering, 2010, 36(5):81-83.)
[50] 赵洪,王芳.理论术语抽取的深度学习模型及自训练算法研究[J].情报学报, 2018, 37(9):923-938.(ZHAO H, WANG F. A deep learning model and self-training algorithm for theoretical terms extraction[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(9):923-938.)
[51] 李刚,朱学芳.面向图博档数字化服务融合的知识图谱构建与实现[J].情报科学, 2021, 39(12):155-164.(LI G, ZHU X F. Construction and implementation of knowledge graph for digital LMA service convergence[J]. Information science, 2021, 39(12):155-164.)
[52] 刘浏,王东波,黄水清,等.数字人文视野下的古汉语实体歧义研究[J].图书与情报, 2020, 195(5):115-124.(LIU L, WANG D B, HUANG S Q, et al. Research on ancient Chinese entity ambiguity in digital humanities[J]. Library&information, 2020, 195(5):115-124.)
