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数据分析与知识发现  2021, Vol. 5 Issue (4): 1-12     https://doi.org/10.11925/infotech.2096-3467.2020.1155
  综述评介 本期目录 | 过刊浏览 | 高级检索 |
基于文献的知识发现新近研究综述 *
代冰,胡正银()
中国科学院成都文献情报中心 成都 610041
中国科学院大学经济与管理学院图书情报与档案管理系 北京 100190
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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摘要 

【目的】 对基于文献的知识发现(Literature-Based Discovery,LBD)近十年的文献进行综述,了解该主题的最新研究进展、发展趋势与面临的挑战。【文献范围】 在Web of Science、CNKI和百度学术中使用“literature based discovery”、“literature AND knowledge discovery”、“文献知识发现”、“文献AND知识挖掘”进行检索,限定文献发表时间为2010年-2020年,共筛选出72篇代表性文献进行述评。【方法】 从研究对象、方法技术、结果评估与典型应用4个方面对文献进行归纳梳理,并总结LBD的发展趋势与面临的挑战。【结果】 LBD发展呈现出研究对象复杂化、分析方法智能化、发现结果丰富化与应用服务实践化的趋势;LBD在多源异构数据融合、知识发现可解释性、结果有效性评估、多领域专家协同方面面临重大挑战。【局限】 主要基于文献对LBD新近进展进行综述,对LBD工具系统及产业界应用覆盖不够。【结论】 作为情报学、信息学、数据科学的交叉研究领域,LBD对挖掘跨学科领域隐性知识与提供高质量学科化知识服务具有重要意义,但真正实现支持潜在的科学新发现还存在诸多挑战。

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代冰
胡正银
关键词 文献挖掘知识发现知识图谱文本挖掘情报研究    
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
收稿日期: 2020-11-04      出版日期: 2020-12-21
ZTFLH:  TP393  
基金资助:*科技部创新方法工作专项(2019IM020100);中国科学院“十三五”信息化专项(XXH13506-203);中国科学院文献情报能力建设专项的研究成果之一(Y9290002)
通讯作者: 胡正银     E-mail: huzy@clas.ac.cn
引用本文:   
代冰,胡正银. 基于文献的知识发现新近研究综述 *[J]. 数据分析与知识发现, 2021, 5(4): 1-12.
Dai Bing,Hu Zhengyin. Review of Studies on Literature-Based Discovery. Data Analysis and Knowledge Discovery, 2021, 5(4): 1-12.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.1155      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/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
Table 1  新近LBD工具系统对比
Fig.1  LBD方法技术分类
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
Table2  新近LBD方法技术比较
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
Table3  新近LBD常用评价方法比较
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