摘要
【目的】围绕“通过科学文献中有关知识主张的文本语言学特征,测度医学知识的不确定性”这一主题,阐述其理论基础、研究进展及其预期应用场景。【文献范围】以同时包含“不确定”、“知识/知识单元”、“医学”三方面关键词为规则,以源作Representing Scientific Knowledge:The Role of Uncertainty设置引文追踪。综合采用关键词检索和引文检索,在中英文数据库检索并筛选文献,共筛选得到51篇。【方法】对文献进行分类述评;对涉及的研究方法、数据来源、核心观点进行归纳梳理。【结果】理论基础主要包括宏观层面的范式转移理论,以及微观层面的统计学理论,如贝叶斯因果网络。研究进展集中于三个方面:一是识别医学文献中表达不确定性的线索词与语句;二是细粒度、结构化表示医学知识对象;三是针对结构化医学知识测度其来源文本表述的不确定性程度。【局限】对知识单元的讨论仅限以“数据-信息-知识-智慧”(Data-Information-Knowledge-Wisdom,DIKW)模型为基本范式的情报学、知识工程或人工智能领域。【结论】医学知识不确定性测度是一个信息计量学与医学信息学交叉研究的新方向。不确定性及其时间演化间接反映知识主张的竞争强度、知识缺口的解决程度和知识确定性的概率,有望促进信息计量学向知识计量学深化,并拓展信息计量学在知识发现、科技评价和人工智能领域潜在的新应用。
[Objective]This article reviewed the theory,research progress and potential applications on measuring uncertainty of medical knowledge from scientific publications.[Coverage]We searched PubMed,Web of Science,Microsoft Academic,CNKI,and Wanfang Data for English and Chinese publications with 1)keywords“uncertain*AND knowledge AND*medical”in title,and 2)the cited reference“Representing Scientific Knowledge:The Role of Uncertainty”.[Methods]First,we categorized these literature into computational linguistics and informetrics studies.Then,we summarized their research design,data analytics and conclusions.[Results]The thoughts of paradigm shift and the Bayesian causal networks were the foundation for measuring uncertainty of medical knowledge.Latest developments included:identifying uncertain cues from biomedical literature;extracting structured knowledge from unstructured biomedical texts;and measuring the uncertainty level of scientific text which resulted Subject-Predicate-Object(SPO)triples.[Limitations]Our discussion focused on the Data-Information-Knowledge-Wisdom driven research,such as information science,knowledge engineering and artificial intelligence.[Conclusions]The uncertainty of scientific knowledge and its evolution over time indirectly reflect the strength of competing knowledge claims,the contribution to fill up knowledge gap,as well as the probability of certainty for a given knowledge claim.It will promote the developments of informetrics and knowmetrics,as well as their applications in emerging fields,such as detecting reserch fronts,evaluating academic contributions and improving the efficacy of computable knowledge driven decision support.
作者
杜建
Du Jian(National Institute of Health Data Science,Peking University,Beijing 100191,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第10期14-27,共14页
Data Analysis and Knowledge Discovery
基金
中国科协青年人才托举工程资助项目“医学知识结构化表示与智能化计算模型研究”(项目编号:2017QNRC001)
北京市高精尖学科建设项目“健康数据科学”(项目编号:BMU2019GJJXK001)的研究成果之一。
关键词
不确定性
医学知识
信息计量学
知识计量学
医学文本挖掘
Uncertainty
Medical Knowledge
Informetrics
Knowledge Metrics(Knowmetrics)
Medical Text Mining
作者简介
通讯作者:杜建,ORCID:0000-0002-7621-9995,E-mail:dujian@bjmu.edu.cn。
