Thilakaratne Menasha, Falkner Katrina, Atapattu Thushari
Faculty of Engineering, Computer and Mathematical Sciences, The University of Adelaide, Adelaide, South Australia, Australia.
PeerJ Comput Sci. 2019 Nov 18;5:e235. doi: 10.7717/peerj-cs.235. eCollection 2019.
As scientific publication rates increase, knowledge acquisition and the research development process have become more complex and time-consuming. Literature-Based Discovery (LBD), supporting automated knowledge discovery, helps facilitate this process by eliciting novel knowledge by analysing existing scientific literature. This systematic review provides a comprehensive overview of the LBD workflow by answering nine research questions related to the major components of the LBD workflow (i.e., input, process, output, and evaluation). With regards to the component, we discuss the data types and data sources used in the literature. The component presents filtering techniques, ranking/thresholding techniques, domains, generalisability levels, and resources. Subsequently, the component focuses on the visualisation techniques used in LBD discipline. As for the component, we outline the evaluation techniques, their generalisability, and the quantitative measures used to validate results. To conclude, we summarise the findings of the review for each component by highlighting the possible future research directions.
随着科学出版物数量的增加,知识获取和研究发展过程变得更加复杂且耗时。基于文献的发现(LBD)支持自动知识发现,通过分析现有科学文献来引出新知识,从而有助于推动这一过程。本系统综述通过回答与LBD工作流程的主要组成部分(即输入、过程、输出和评估)相关的九个研究问题,对LBD工作流程进行了全面概述。关于输入组件,我们讨论了文献中使用的数据类型和数据源。过程组件介绍了过滤技术、排名/阈值技术、领域、通用性水平和资源。随后,输出组件重点关注LBD学科中使用的可视化技术。至于评估组件,我们概述了评估技术、它们的通用性以及用于验证结果的定量措施。最后,我们通过强调可能的未来研究方向,总结了每个组件的综述结果。