A systematic review on literature-based discovery workflow

被引:25
作者
Thilakaratne, Menasha [1 ]
Falkner, Katrina [1 ]
Atapattu, Thushari [1 ]
机构
[1] Univ Adelaide, Fac Engn Comp & Math Sci, Adelaide, SA, Australia
关键词
Literature-Based Discovery; Literature Mining; Knowledge Discovery; Systematic Review; COMPLEMENTARY LITERATURES; HYPOTHESIS GENERATION; POTENTIAL TREATMENTS; ALZHEIMERS-DISEASE; LINK PREDICTION; ASSOCIATION; CONNECTIONS; KNOWLEDGE; NETWORK; RAYNAUDS;
D O I
10.7717/peerj-cs.235
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 input component, we discuss the data types and data sources used in the literature. The process component presents filtering techniques, ranking/thresholding techniques, domains, generalisability levels, and resources. Subsequently, the output component focuses on the visualisation techniques used in LBD discipline. As for the evaluation 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.
引用
收藏
页码:1 / 40
页数:40
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