基于文献的知识发现新近研究综述
Review of Studies on Literature-Based Discovery
查看参考文献87篇
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文摘
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【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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其他语种文摘
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[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. |
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来源
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数据分析与知识发现
,2021,5(4):1-12 【扩展库】
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DOI
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10.11925/infotech.2096-3467.2020.1155
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关键词
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文献挖掘
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知识发现
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知识图谱
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文本挖掘
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情报研究
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地址
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1.
中国科学院成都文献情报中心, 成都, 610041
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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
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语种
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中文 |
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文献类型
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综述型 |
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ISSN
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2096-3467 |
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学科
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社会科学总论;自动化技术、计算机技术 |
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基金
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科技部创新方法工作专项
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中国科学院“十三五”信息化专项
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中国科学院文献情报能力建设专项
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文献收藏号
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CSCD:6964270
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