State University of New York at Buffalo, NY, United States.
University of Virginia, VA, United States.
J Biomed Inform. 2019 May;93:103141. doi: 10.1016/j.jbi.2019.103141. Epub 2019 Mar 9.
Literature Based Discovery (LBD) refers to the problem of inferring new and interesting knowledge by logically connecting independent fragments of information units through explicit or implicit means. This area of research, which incorporates techniques from Natural Language Processing (NLP), Information Retrieval and Artificial Intelligence, has significant potential to reduce discovery time in biomedical research fields. Formally introduced in 1986, LBD has grown to be a significant and a core task for text mining practitioners in the biomedical domain. Together with its inter-disciplinary nature, this has led researchers across domains to contribute in advancing this field of study. This survey attempts to consolidate and present the evolution of techniques in this area. We cover a variety of techniques and provide a detailed description of the problem setting, the intuition, the advantages and limitations of various influential papers. We also list the current bottlenecks in this field and provide a general direction of research activities for the future. In an effort to be comprehensive and for ease of reference for off-the-shelf users, we also list many publicly available tools for LBD. We hope this survey will act as a guide to both academic and industry (bio)-informaticians, introduce the various methodologies currently employed and also the challenges yet to be tackled.
文献基础发现（LBD）是指通过显式或隐式的方式将独立的信息单元片段通过逻辑连接来推断新的和有趣的知识的问题。该研究领域融合了自然语言处理（NLP）、信息检索和人工智能技术，在生物医学研究领域具有显著缩短发现时间的潜力。LBD 于 1986 年正式提出，现已成为生物医学领域文本挖掘从业者的一项重要核心任务。由于其跨学科性质，吸引了不同领域的研究人员为该领域的研究做出贡献。本调查试图对该领域的技术发展进行总结和呈现。我们涵盖了各种技术，并详细描述了问题设置、直觉、各种有影响力的论文的优点和局限性。我们还列出了该领域当前的瓶颈，并为未来的研究活动提供了一个总体方向。为了全面性和便于参考，我们还列出了许多用于 LBD 的现成工具。我们希望本调查能为学术和工业（生物）信息学家提供指导，介绍当前使用的各种方法，以及尚未解决的挑战。
