%A Dai Bing,Hu Zhengyin %T Review of Studies on Literature-Based Discovery %0 Journal Article %D 2021 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2020.1155 %P 1-12 %V 5 %N 4 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4968.shtml} %8 2021-04-25 %X

[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.