Knowledge extraction for literature review

Researchers in all domains need to keep abreast with recent scientific advances. Finding relevant publications and reviewing them is a labor-intensive task that lacks efficient automatic tools to support it. Current tools are limited to standard keyword-based search systems that return potentially relevant documents and then leave the user with a monumental task of sifting through them. In this paper, we present a semantic-driven system to automatically extract the most important knowledge from a publication and reduces the effort required for the literature review. The system extracts key findings from biomedical papers in PubMed, populates a predefined template and displays it. This allows the user to get the key ideas of the content even before opening or downloading the publication.

所有领域的研究人员都需要跟上最新的科学进展。查找相关的出版物并对其进行综述是一项劳动密集型的任务，缺乏高效的自动化工具来提供支持。目前的工具仅限于基于标准关键词的搜索系统，这些系统会返回可能相关的文档，然后留给用户一项艰巨的筛选任务。在本文中，我们提出了一个语义驱动的系统，用于自动从出版物中提取最重要的知识，并减少文献综述所需的工作量。该系统从PubMed中的生物医学论文中提取关键发现，填充预定义的模板并进行展示。这使得用户甚至在打开或下载出版物之前就能了解其内容的关键要点。