Page 100 - 《中国图书馆学报》2025年第1期
P. 100

098                  Journal of Library Science in China






           [ 2 ]  MONDAL I,HOU Y,JOCHIM C. End-to-end construction of NLP knowledge graph[C] / / Findings of the Association

                for Computational Linguistics:ACL-IJCNLP 2021. Association of Computational Linguistics,2021:1885-1895.

           [ 3 ]  HOUNGBO H,MERCER R E. Method mention extraction from scientific research papers [ C] / / Proceedings of

                COLING 2012. Mumbai:The COLING 2012 Organizing Committee,2012:1211-1222.

           [ 4 ]  章成志,张颖怡. 基于学术论文全文的研究方法实体自动识别研究[J]. 情报学报,2020,39(6):589- 600.

                (ZHANG C Z,ZHANG Y Y. Automatic recognition of research methods from the full-text of academic articles[J].

                Journal of the China Society for Scientific and Technical Information,2020,39(6):589-600. )

                     ˇ   '          '               '
           [ 5 ]  KOVACEVIC A,KONJOVIC Z,MILOSAVLJEVIC B,et al. Mining methodologies from NLP publications:a case study

                in automatic terminology recognition[J]. Computer Speech & Language,2012,26(2):105-126.

           [ 6 ]  HEFFERNAN K,TEUFEL S. Identifying problems and solutions in scientific text[J]. Scientometrics,2018,116(2):
                1367-1382.

           [ 7 ]  许天祥. 面向“问题—方法”利用的科技文本知识挖掘研究[D]. 南京:南京理工大学,2020. (XU T X. Research

                on knowledge mining of scientific and technological texts for “question-method” utilization[ D]. Nanjing:Nanjing

                University of Science & Technology,2020. )

           [ 8 ]  钱力,张晓林,王茜. 科技论文的研究设计指纹自动识别方法构建与实现[J]. 图书情报工作,2018,62(2):

                135-143. (QIAN L,ZHANG X L,WANG Q. Building and implement on automatic identification method of research

                design fingerprint of scientific papers[J]. Library and Information Service,2018,62(2):135-143. )

           [ 9 ]  任海英,石彤. 科技论文微观概念地图的构建及研究思路的挖掘[J]. 图书情报工作,2016,60(4):115-124.

                (REN H Y,SHI T. The construction of microscopic concept and the mining of research ideas in scientific papers[J].

                Library and Information Service,2016,60(4):115-124. )

           [10]  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. )

           [11]  FÄRBER M. The Microsoft academic knowledge graph:a linked data source with 8 billion triples of scholarly data

                [C] / / 18th International Semantic Web Conference. Cham:Springer,2019:113-129.

           [12]  虞松涛. 基于学术知识图谱的语义检索研究[D]. 武汉:华中师范大学,2020. (YU S T. A research of semantic

                retrieval based on academic knowledge graph[D]. Wuhan:Central China Normal University,2020. )

           [13]  CHANG C,TANG Y,LONG Y,et al. Multi-information preprocessing event extraction with BiLSTM-CRF attention for

                academic knowledge graph construction[J]. IEEE Transactions on Computational Social Systems,2023,10(5):2713-
                2724.

           [14]  孙浩然. 基于学术知识图谱的话题演化分析方法[ D]. 上海:上海交通大学,2019. ( SUN H R. Academic

                knowledge graph based topic evolution analysis method[D]. Shanghai:Shanghai Jiao Tong University,2019. )

           [15]  曹树金,曹茹烨. 基于知识图谱支持科研创新的跨学科知识发现研究[J]. 情报理论与实践,2022,45(11):10-

                20. (CAO S J,CAO R Y. Research on interdisciplinary knowledge discovery based on knowledge graph to support

                scientific research innovation[J]. Information Studies:Theory & Application,2022,45(11):10-20. )

           [16]  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 BiLSTM-CRF model[J]. Journal of the China Society for Scientific and Technical Information,2020,

           总第五十一卷  第二七五期  Vol. 51. No. 275
   95   96   97   98   99   100   101   102   103   104   105