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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (4): 1-12    DOI: 10.11925/infotech.2096-3467.2020.1155
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Review of Studies on Literature-Based Discovery
Dai Bing,Hu Zhengyin()
Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
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Abstract  

[Objective] This paper reviews literature-based discovery (LBD) studies, aiming to explore the latest progress, development trends and challenges in this field. [Coverage] We searched “literature-based discovery” or “literature and knowledge discovery” in Chinese and English with the Web of Science, CNKI and Baidu Academic for research published from 2010 to 2020. A total of 72 representative literature were chosen for review. [Methods] Firstly, we summarized these studies from research objects, methods and techniques, results and typical applications. We then discussed future development trends and challenges facing LBD. [Results] The research objects of LBD were becoming complicated, while the analysis methods and techniques were more intelligent. The discovery results were further enriched, which led to more LBD applications. There are some challenges facing LBD, such as multi-source heterogeneous data fusion, interpretability of knowledge discovery, evaluation of results, and collaboration of multi-disciplinary experts. [Limitations] We did not examine LBD tools / systems as well as industry applications extensively. [Conclusions] As an interdisciplinary research field of information science, informatics and data science, LBD is of great significance for mining knowledge and providing high-quality subject knowledge services.

Key wordsLiterature Mining      Knowledge Discovery      Knowledge Graph      Text Mining      Information Research     
Received: 04 November 2020      Published: 21 December 2020
ZTFLH:  TP393  
Fund:Ministry of Science and Technology Innovation Methods Special Project(2019IM020100);Informationization Special Project of Chinese Academy of Sciences(XXH13506-203);Literature and Information Capacity Building Special Project of Chinese Academy of Sciences(Y9290002)
Corresponding Authors: Hu Zhengyin     E-mail: huzy@clas.ac.cn

Cite this article:

Dai Bing,Hu Zhengyin. Review of Studies on Literature-Based Discovery. Data Analysis and Knowledge Discovery, 2021, 5(4): 1-12.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1155     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I4/1

出现年代 系统名称 知识发现模型 数据源 知识对象 分析方法
2010 EpiphaNet[18] ABC模型 科技文献(MEDLINE) 科技术语、多维向量、
三元组
共现分析、语义分析
2010 Semantic BITOLA[22] ABC模型 科技文献(MEDLINE)、科学数据(GEO) 科技术语、三元组 共现分析、语义分析
2012 CmedLBKD[24] ABC模型 科技文献(PubMed和中国生物医学文献数据库) 科技术语 共现分析
2015 Obvio[11] AnC模型 科技文献(MEDLINE)、三元组数据(SemMedDB)、科学数据(BKR) 科技术语、三元组、
知识路径、知识子图
语义分析、知识图谱
2019 LION LBD[23] AnC模型 科技文献(PubMed)、科学数据(NCBI基因数据库)、本体(NCBI Taxonomy) 科技术语、三元组、
知识路径、知识子图
语义分析、知识图谱、机器学习
1997 ArrowSmith[21] ABC模型 科技文献(MEDLINE) 科技术语 共现分析
Comparison of Recent LBD Systems
A Diagram of LBD Methods and Techniques
方法 关键技术 优点 缺点 适用场景
基于共现的方法 共现频率[2,27]、稀有知识共现[28,29] 简单、直观、易实现 未考虑知识实体间除共现外的其他语义关系;分析的知识对象类型单一 数据量较小、学科领域较单一、单一类型知识对象、浅层次知识发现
基于语义的方法 语义过滤[30,31]、发现模式[22,32-33]、语义向量[16-17,34-35] 知识实体间具有丰富的语义关系、知识发现结果可解释性好 依赖领域本体与专家资源,需较多人工参与,分析的知识对象类型单一 数据量适中、学科领域较单一、单一类型知识对象、较复杂知识发现
基于知识图谱方法 路径挖掘与子图挖掘[2,10-11,37-40]、链路预测[41,42,43]、引文网络[44,45,46,47,48] 可分析多种类型的知识对象、跨学科领域、复杂的隐性知识发现 知识图谱构建成本较高,知识发现结果可解释性较差 数据量较大、跨学科领域、多类型知识对象、深层次复杂知识发现
融合人工智能的方法 机器学习[49,50,51,52]、深度学习[53,54,55] 人工参与少、减少用户偏见与先验知识的限制 模型训练需要大量高质量数据,知识发现过程不透明,挖掘结果需要人工解读 海量数据、跨学科领域、多类型知识对象、深层次复杂知识发现
Comparison of Recent LBD Methods and Techniques
评估方法 具体评估方式 领域依赖性 优点 缺点
对比评价法 与先前工作比较[56]、复现Swanson发现[57,58]、与选定数据库比较[44,59-60] 领域依赖 容易实现,可揭示现有系统和方法的缺陷 依赖领域数据库,通用性差,评估作用有限
基于证据的评价方法 时间片划分方法[61,62] 不依赖领域 客观、自动化,可重复测试,通用性好,容易操作 时间片选取存在主观性,只评估目标结果,无法评价未来的知识发现结果
专家评价法 专家打分[66]或专家人工制定参考结果[67] 不依赖领域 专家参与 成本高,主观、片面
Comparison of Recent LBD Evaluation Methods
[1] 马明, 武夷山. Don R. Swanson的情报学学术成就的方法论意义与启示[J]. 情报学报, 2003,22(3):259-266.
[1] ( Ma Ming, Wu Yishan. Methodological Enlightenment and Significance of Don R.Swanson’s Achievements in Information Science[J]. Journal of the China Society for Scientific and Technical Information, 2003,22(3):259-266.)
[2] 胡正银, 刘蕾蕾, 代冰, 等. 基于领域知识图谱的生命医学学科知识发现探析[J]. 数据分析与知识发现, 2020,4(11):1-14.
[2] ( Hu Zhengyin, Liu Leilei, Dai Bing, et al. Discovering Subject Knowledge in Life and Medical Sciences with Knowledge Graph[J]. Data Analysis and Knowledge Discovery, 2020,4(11):1-14.)
[3] Hey T, Tansley S, Tolle K. The Fourth Paradigm: Data-intensive Scientific Discovery[M]. Redmond, WA: Microsoft Research, 2009.
[4] Ganiz M C, Pottenger W M, Janneck C D. Recent Advances in Literature Based Discovery[EB/OL]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.77.6842&rep=rep1&type=pdf.
[5] Hui W, Lau W K. Application of Literature-Based Discovery in Nonmedical Disciplines: A Survey[C]// Proceedings of the 2nd International Conference on Computing and Big Data. 2019: 7-11.
[6] Swanson D R. Fish Oil, Raynaud’s Syndrome, Undiscovered Public Knowledge[J]. Perspectives in Biology and Medicine, 1986,30(1):7-18.
doi: 10.1353/pbm.1986.0087
[7] Swanson D R. Undiscovered Public Knowledge[J]. The Library Quarterly, 1986,56(2):103-118.
doi: 10.1086/601720
[8] Gopalakrishnan V, Jha K, Jin W, et al. A Survey on Literature Based Discovery Approaches in Biomedical Domain[J]. Journal of Biomedical Informatics, 2019,93:103141.
doi: S1532-0464(19)30059-0 pmid: 30857950
[9] Smalheiser N R. Literature-Based Discovery: Beyond the ABCs[J]. Journal of the American Society for Information Science and Technology, 2012,63(2):218-224.
doi: 10.1002/asi.v63.2
[10] Wilkowski B, Fiszman M, Miller C M, et al. Graph-Based Methods for Discovery Browsing with Semantic Predications[C]// Proceedings of the 2011 AMIA Annual Symposium. 2011.
[11] Cameron D, Kavuluru R, Rindflesch T C, et al. Context-Driven Automatic Subgraph Creation for Literature-Based Discovery[J]. Journal of Biomedical Informatics, 2015,54:141-157.
doi: 10.1016/j.jbi.2015.01.014 pmid: 25661592
[12] 桑盛田. 生物医学文献中的隐含知识发现方法研究[D]. 大连: 大连理工大学, 2019.
[12] ( Sang Shengtian. Research on Literature Based Discovery Methods in Biomedical Literature[D]. Dalian: Dalian University of Technology, 2019.)
[13] Vicente-Gomila J M. The Contribution of Syntactic-Semantic Approach to the Search for Complementary Literatures for Scientific or Technical Discovery[J]. Scientometrics, 2014,100(3):659-673.
doi: 10.1007/s11192-014-1299-2
[14] 余黄樱子, 董庆兴, 张斌. 基于网络表示学习的疾病知识关联挖掘与预测方法研究[J]. 情报理论与实践, 2019,42(12):156-162.
[14] ( Yu Huangyingzi, Dong Qingxing, Zhang Bin. Disease Knowledge Association Mining and Forecasting Based on Network Representation Learning[J]. Information Studies: Theory & Application, 2019,42(12):156-162.)
[15] Maclean D, Seltzer M I. Mining the Web for Medical Hypoconfproc: A Proof-of-Concept System[C]// Proceedings of the 2011 International Conference on Health Informatics. 2012.
[16] Cohen T, Widdows D, Schvaneveldt R W, et al. Discovering Discovery Patterns with Predication-Based Semantic Indexing[J]. Journal of Biomedical Informatics, 2012,45(6):1049-1065.
doi: 10.1016/j.jbi.2012.07.003
[17] Cohen T, Widdows D, Stephan C, et al. Predicting High-throughput Screening Results with Scalable Literature-Based Discovery Methods[J]. CPT: Pharmacometrics & Systems Pharmacology, 2014,3(10):e140.
[18] Cohen T, Whitfield G K, Schvaneveldt R W, et al. EpiphaNet: An Interactive Tool to Support Biomedical Discoveries[J]. Journal of Biomedical Discovery and Collaboration, 2010,5(1):21-49.
[19] Hu Z Y, Dai B, Zhang Y, et al. Mining Latent Relations Between Disease and Transcription Factor Based on Knowledge Graph: A Case Study on Alzheimer’s Disease[C]// Proceedings of the 10th Global TechMining Conference. 2020.
[20] Liu C, Chu W W, Sabb F, et al. Path Knowledge Discovery: Association Mining Based on Multi-Category Lexicons[C]// Proceedings of the 2014 IEEE International Conference on Big Data. IEEE, 2014: 1049-1059.
[21] Swanson D R, Smalheiser N R. An Interactive System for Finding Complementary Literatures: A Stimulus to Scientific Discovery[J]. Artificial Intelligence, 1997,91(2):183-203.
doi: 10.1016/S0004-3702(97)00008-8
[22] Hristovski D, Kastrin A, Peterlin B, et al. Combining Semantic Relations and DNA Microarray Data for Novel Hypotheses Generation[A]// Linking Literature, Information, and Knowledge for Biology[M]. Springer, Berlin, Heidelberg, 2010: 53-61.
[23] Pyysalo S, Baker S, Ali I, et al. LION LBD: A Literature-Based Discovery System for Cancer Biology[J]. Bioinformatics, 2019,35(9):1553-1561.
doi: 10.1093/bioinformatics/bty845 pmid: 30304355
[24] 钱庆, 洪娜, 李勇, 等. 中文非相关文献知识发现系统CmedLBKD构建[J]. 情报理论与实践, 2012,35(4):109-113.
[24] ( Qian Qing, Hong Na, Li Yong, et al. Construction of the Chinese Disjoint Literature-Based Knowledge Discovery System CmedLBKD[J]. Information Studies: Theory & Application, 2012,35(4):109-113.)
[25] Sebastian Y, Siew E G, Orimaye S O. Emerging Approaches in Literature-Based Discovery: Techniques and Performance Review[J]. The Knowledge Engineering Review, 2017,32: Article No. e12. DOI: https://doi.org/10.1017/S0269888917000042.
[26] Thilakaratne M, Falkner K, Atapattu T. A Systematic Review on Literature-Based Discovery: General Overview, Methodology, & Statistical Analysis[J]. ACM Computing Surveys, 2019,52(6):1-34.
[27] Petrič I, Cestnik B, Lavrač N, et al. Outlier Detection in Cross-Context Link Discovery for Creative Literature Mining[J]. The Computer Journal, 2012,55(1):47-61.
doi: 10.1093/comjnl/bxq074
[28] Petriĕ I, Urbanĕiĕ T, Cestnik B, et al. Literature Mining Method RaJoLink for Uncovering Relations Between Biomedical Concepts[J]. Journal of Biomedical Informatics, 2009,42(2):219-227.
doi: 10.1016/j.jbi.2008.08.004
[29] Workman T E, Fiszman M, Cairelli M J, et al. Spark, An Application Based on Serendipitous Knowledge Discovery[J]. Journal of Biomedical Informatics, 2016,60:23-37.
doi: 10.1016/j.jbi.2015.12.014
[30] Banerjee R, Ramakrishnan I, Choi Y, et al. Automated Suggestion of Tests for Identifying Likelihood of Adverse Drug Events[C]// Proceedings of the 2014 IEEE International Conference on Healthcare Informatics. 2014: 170-176.
[31] 李宗耀, 杨志豪, 吴晓芳, 等. 基于语义资源的生物医学文献知识发现研究[J]. 中文信息学报, 2016,30(1):176-182.
[31] ( Li Zongyao, Yang Zhihao, Wu Xiaofang, et al. Using Semantic Relations for Biomedical Literature-Based Discovery[J]. Journal of Chinese Information Processing, 2016,30(1):176-182.)
[32] Hristovski D, Friedman C, Rindflesch T C, et al. Exploiting Semantic Relations for Literature-Based Discovery[C]// Proceedings of the 2006 AMIA Annual Symposium. 2006: 349-353.
[33] Ahlers C B, Hristovski D, Kilicoglu H, et al. Using the Literature-Based Discovery Paradigm to Investigate Drug Mechanisms[C]// Proceedings of the 2007 AMIA Annual Symposium. 2007: 6-10.
[34] Mower J, Subramanian D, Shang N, et al. Classification-by-analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships[C]// Proceedings of the 2016 AMIA Annual Symposium. 2016: 1940-1949.
[35] Symonds M, Bruza P D, Sitbon L. The Efficiency of Corpus-Based Distributional Models for Literature-Based Discovery on Large Data Sets[C]// Proceedings of the 2nd Australasian Web Conference. 2014: 49-57.
[36] Zhao H, Yao Q, Li J, et al. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks[C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017: 635-644.
[37] Dharmavaram S, Shaik A, Jin W. Mining Biomedical Data for Hidden Relationship Discovery[C]// Proceedings of the 2019 IEEE International Conference on Healthcare Informatics. IEEE, 2019: 1-10.
[38] Jha K, Jin W. Mining Hidden Knowledge from the Counterterrorism Dataset Using Graph-Based Approach[C]// Proceedings of the 21st International Conference on Applications of Natural Language to Information Systems. Springer, Cham, 2016: 310-317.
[39] Sang S, Yang Z, Liu X, et al. GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery from Biomedical Literatures[J]. IEEE Access, 2018,7:8404-8415.
doi: 10.1109/ACCESS.2018.2886311
[40] 杜建. 医学知识不确定性测度的进展与展望[J]. 数据分析与知识发现, 2020,4(10):14-27.
[40] ( Du Jian. Measuring Uncertainty of Medical Knowledge: A Literature Review[J]. Data Analysis and Knowledge Discovery, 2020,4(10):14-27.)
[41] Kastrin A, Rindflesch T C, Hristovski D. Link Prediction on a Network of Co-Occurring MeSH Terms: Towards Literature-Based Discovery[J]. Methods of Information in Medicine, 2016,55(4):340-346.
doi: 10.3414/ME15-01-0108 pmid: 27435341
[42] Sebastian Y, Siew E G, Orimaye S O. Learning the Heterogeneous Bibliographic Information Network for Literature-Based Discovery[J]. Knowledge-Based Systems, 2017,115:66-79.
doi: 10.1016/j.knosys.2016.10.015
[43] 陈亮, 彭喆. 基于专利异构网络的中小企业潜在合作伙伴发现[A]//张志强, 胡正银, 文奕. 学科信息学与学科知识发现[M]. 北京:科学出版社, 2020.
[43] ( Chen Liang, Peng Zhe. Discovery of Potential Partners of SMEs Based on Patent Heterogeneous Network[A]//Zhang Zhiqiang, Hu Zhengyin, Wen Yi. Subject Informatics and Subject Knowledge Discovery[M]. Beijing:Science Press, 2020.)
[44] Ding Y, Song M, Han J, et al. Entitymetrics: Measuring the Impact of Entities[J]. PLoS One, 2013,8(8):e71416.
doi: 10.1371/journal.pone.0071416
[45] Kostoff R N. Literature-Related Discovery: Common Factors for Parkinson’s Disease and Crohn’s Disease[J]. Scientometrics, 2014,100(3):623-657.
doi: 10.1007/s11192-014-1298-3
[46] Henry S, Panahi A, Wijesinghe D S, et al. A Literature Based Discovery Visualization System with Hierarchical Clustering and Linking Set Associations[J]. AMIA Summits on Translational Science Proceedings, 2019: 582-591.
[47] Fujita K. Finding Linkage Between Sustainability Science and Technologies Based on Citation Network Analysis[C]// Proceedings of the 5th IEEE International Conference on Service-Oriented Computing and Applications. IEEE, 2012: 1-6.
[48] Ittipanuvat V, Fujita K, Sakata I, et al. Finding Linkage Between Technology and Social Issue: A Literature Based Discovery Approach[J]. Journal of Engineering and Technology Management, 2014,32:160-184.
doi: 10.1016/j.jengtecman.2013.05.006
[49] Crichton G K O. Improving Automated Literature-Based Discovery with Neural Networks: Neural Biomedical Named Entity Recognition, Link Prediction and Discovery[D]. London: University of Cambridge, 2019.
[50] Sang S, Yang Z, Li Z, et al. Supervised Learning Based Hypojournal Generation from Biomedical Literature[J]. BioMed Research International, 2015. DOI: 10.1155/2015/698527.
[51] Xu B, Shi X, Zhao Z, et al. Leveraging Biomedical Resources in Bi-LSTM for Drug-Drug Interaction Extraction[J]. IEEE Access, 2018,6:33432-33439.
doi: 10.1109/ACCESS.2018.2845840
[52] Choudhury N, Faisal F, Khushi M. Towards an LSTM-Based Predictive Framework for Literature-Based Knowledge Discovery[OL]. arXiv Preprint, arXiv: 1907. 09395.
[53] Xun G, Jha K, Gopalakrishnan V, et al. Generating Medical Hypotheses Based on Evolutionary Medical Concepts[C]// Proceedings of the 2017 IEEE International Conference on Data Mining. IEEE, 2017: 535-544.
[54] 刘景陶, 柳耀花. 计算机分子模拟技术及人工智能在药物研发中的应用[J]. 科技创新与应用, 2018(2):46-47.
[54] ( Liu Jingtao, Liu Yaohua. Application of Computer Molecular Simulation Technology and Artificial Intelligence in Drug Development[J]. Technology Innovation and Application, 2018(2):46-47.)
[55] Brown N, Cambruzzi J, Cox P J, et al. Big Data in Drug Discovery[J]. Progress in Medicinal Chemistry, 2018,57:277-356.
[56] Lever J, Gakkhar S, Gottlieb M, et al. A Collaborative Filtering-Based Approach to Biomedical Knowledge Discovery[J]. Bioinformatics, 2018,34(4):652-659.
doi: 10.1093/bioinformatics/btx613 pmid: 29028901
[57] Gordon M D, Lindsay R K. Toward Discovery Support Systems: A Replication, Re-Examination, and Extension of Swanson’s Work on Literature-Based Discovery of a Connection Between Raynaud’s and Fish Oil[J]. Journal of the American Society for Information Science, 1996,47(2):116-128.
doi: 10.1002/(ISSN)1097-4571
[58] Srinivasan P. Text Mining: Generating Hypotheses from MEDLINE[J]. Journal of the American Society for Information Science and Technology, 2004,55(5):396-413.
doi: 10.1002/(ISSN)1532-2890
[59] Rastegar-Mojarad M, Elayavilli R K, Wang L, et al. Prioritizing Adverse Drug Reaction and Drug Repositioning Candidates Generated by Literature-Based Discovery[C]// Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. 2016: 289-296.
[60] Shang N, Xu H, Rindflesch T C, et al. Identifying Plausible Adverse Drug Reactions Using Knowledge Extracted from the Literature[J]. Journal of Biomedical Informatics, 2014,52:293-310.
doi: 10.1016/j.jbi.2014.07.011 pmid: 25046831
[61] Hristovski D, Stare J, Peterlin B, et al. Supporting Discovery in Medicine by Association Rule Mining in Medline and UMLS[J]. Studies in Health Technology and Informatics, 2001(2):1344-1348.
[62] Yetisgen-Yildiz M, Pratt W. Using Statistical and Knowledge-Based Approaches for Literature-Based Discovery[J]. Journal of Biomedical Informatics, 2006,39(6):600-611.
pmid: 16442852
[63] 祝清松, 冷伏海. 非相关文献知识发现的融合评价方法构建研究[J]. 情报理论与实践, 2013,36(7):106-109, 105.
[63] ( Zhu Qingsong, Leng Fuhai. Research on the Development of the Fusion Evaluation Method for the Disjoint Literature-Based Knowledge Discovery[J]. Information Studies: Theory & Application, 2013,36(7):106-109, 105.)
[64] 钟丽萍, 冷伏海. 非相关文献知识发现有效性评价的研究现状及评述[J]. 情报理论与实践, 2011,34(5):121-125.
[64] ( Zhong Liping, Leng Fuhai. Research Status and Review of the Effectiveness Evaluation for the Disjoint Literature-Based Knowledge Discovery[J]. Information Studies: Theory & Application, 2011,34(5):121-125.)
[65] Baek S H, Lee D, Kim M, et al. Enriching Plausible New Hypojournal Generation in PubMed[J]. PLoS One, 2017,12(7):e0180539.
doi: 10.1371/journal.pone.0180539
[66] Srinivasan P, Libbus B. Mining MEDLINE for Implicit Links Between Dietary Substances and Diseases[J]. Bioinformatics, 2004,20(S1):i290-i296.
doi: 10.1093/bioinformatics/bth914
[67] Gordon M, Lindsay R K, Fan W. Literature-based Discovery on the World Wide Web[J]. ACM Transactions on Internet Technology, 2002,2(4):261-275.
doi: 10.1145/604596.604597
[68] Spangler S, Wilkins A D, Bachman B J, et al. Automated Hypothesis Generation Based on Mining Scientific Literature[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014: 1877-1886.
[69] Sang S, Yang Z, Wang L, et al. SemaTyP: A Knowledge Graph Based Literature Mining Method for Drug Discovery[J]. BMC Bioinformatics, 2018,19(1): ArticleNo. 193.
doi: 10.1186/s12859-018-2167-5
[70] Zhang R, Cairelli M J, Fiszman M, et al. Exploiting Literature-Derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs[J]. Cancer Informatics, 2014,13(S1):103-111.
[71] Yang H T, Ju J H, Wong Y T, et al. Literature-Based Discovery of New Candidates for Drug Repurposing[J]. Briefings in Bioinformatics, 2017,18(3):488-497.
[72] Henry S, McInnes B T. Literature Based Discovery: Models, Methods, and Trends[J]. Journal of Biomedical Informatics, 2017,74:20-32.
doi: S1532-0464(17)30190-9 pmid: 28838802
[73] Hristovski D, Kastrin A, Dinevski D, et al. Using Literature-Based Discovery to Explain Adverse Drug Effects[J]. Journal of Medical Systems, 2016, 40(8): Article No. 185.
doi: 10.1007/s10916-016-0544-z pmid: 27318993
[74] Lamurias A, Ferreira J D, Clarke L A, et al. Generating a Tolerogenic Cell Therapy Knowledge Graph from Literature[J]. Frontiers in Immunology, 2017,8:1656.
doi: 10.3389/fimmu.2017.01656 pmid: 29238346
[75] 王雪, 武俊伟, 陈观群, 等. 阿尔茨海默病基因-疾病关联的知识挖掘[J]. 图书情报工作, 2020,64(13):120-132.
[75] ( Wang Xue, Wu Junwei, Chen Guanqun, et al. Knowledge Mining of Alzheimer’s Disease Gene-Disease Associations[J]. Library and Information Service, 2020,64(13):120-132.)
[76] 牟冬梅, 金姗, 琚沅红. 基于文献数据的疾病与基因关联关系研究[J]. 数据分析与知识发现, 2018,2(8):98-106.
[76] ( Mu Dongmei, Jin Shan, Ju Yuanhong. Finding Association Between Diseases and Genes from Literature Abstracts[J]. Data Analysis and Knowledge Discovery, 2018,2(8):98-106.)
[77] Özgür A, Xiang Z, Radev D R, et al. Literature-Based Discovery of IFN-γ and Vaccine-Mediated Gene Interaction Networks[J]. of Biomedicine and Biotechnology, 2010(19):426479.
[78] Srinivasan M, Blackburn C, Mohamed M, et al. Literature-Based Discovery of Salivary Biomarkers for Type 2 Diabetes Mellitus[J]. Biomarker Insights, 2015,10:39-45.
doi: 10.4137/BMI.S22177 pmid: 26005324
[79] Hristovski D, Kastrin A, Rindflesch T C. Semantics-Based Cross-domain Collaboration Recommendation in the Life Sciences: Preliminary Results[C]// Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2015: 805-806.
[80] Porter A, Zhang Y, Huang Y, et al. Tracking and Mining the COVID-19 Research Literature[J]. Frontiers in Research Metrics and Analytics, 2020,5. DOI: 10.3389/frma.2020.594060.
[81] Wu M J, Zhang Y, Zhang G Q, et al. Exploring the Genetic Basis for Diseases Through a Heterogeneous Bibliometric Network: A Methodology and Case Study[J]. Technological Forecasting and Social Change, 2021,164: Article No. 120513.
doi: 10.1016/j.techfore.2020.120513
[82] 李文林, 曾莉, 杨斓. 基于文献的知识发现服务及其问题——以南京中医药大学图书馆为例[J]. 大学图书馆学报, 2015,33(2):61-65.
[82] ( Li Wenlin, Zeng Li, Yang Lan. Experiences and Problems in Literature-based Knowledge Discovery Service in University Libraries——Taking Nanjing University of Chinese Medicine Library as an Example[J]. Journal of Academic Libraries, 2015,33(2):61-65.)
[83] 刘小慧, 李长玲, 崔斌, 等. 基于闭合式非相关知识发现的潜在跨学科合作研究主题识别——以情报学与计算机科学为例[J]. 情报理论与实践, 2017,40(9):71-76.
[83] ( Liu Xiaohui, Li Changling, Cui Bin, et al. Research Topics Identification of Potential Interdisciplinary Collaboration Based on Closed and Irrelevant Knowledge Discovery[J]. Information Studies: Theory & Application, 2017,40(9):71-76.)
[84] 李长玲, 刘小慧, 刘运梅, 等. 基于开放式非相关知识发现的潜在跨学科合作研究主题识别——以情报学与计算机科学为例[J]. 情报理论与实践, 2018,41(2):100-104,137.
[84] ( Li Changling, Liu Xiaohui, Liu Yunmei, et al. Identifying Potential Disciplinary Collaboration Research Topics by Open Literature-based Discovery: Taking Information Science and Computer Science as Examples[J]. Information Studies: Theory & Application, 2018,41(2):100-104,137.)
[85] Hu Z Y, Xu H Y, Tan X C. A Knowledge Graph of Stem Cell Oriented to Subject Knowledge Discovery[C]// Proceedings of the 7th IEEE International Conference on Healthcare Informatics. 2019.
[86] Smalheiser N R. Rediscovering Don Swanson: The Past, Present and Future of Literature-based Discovery[J]. Journal of Data & Information Science, 2017,2(4):45-66.
[87] 胡正银. 基于个性化语义TRIZ的专利技术挖掘研究[D]. 北京:中国科学院大学, 2015.
[87] ( Hu Zhengyin. Study on Patent Tech Mining Based on Personalized Semantic TRIZ[D]. Beijing: University of Chinese Academy of Sciences, 2015.)
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