论文标题
从Covid-19论文中提取机制的知识库
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
论文作者
论文摘要
COVID-19大流行催生了各种各样的科学文献，这些文献挑战，刺激了对自动化工具的兴趣，以帮助找到有用的知识。我们追求一个知识库（KB）的机制，这是一个涵盖活动，功能和因果关系的基本概念，从蜂窝过程到经济影响。我们通过开发广泛的统一模式从相关性和广度之间取得平衡来从科学论文的自然语言中提取这些信息。我们用我们的模式注释了机制的数据集，并训练模型从论文中提取机制关系。我们的实验证明了我们KB在支持Covid-19文献的跨学科科学搜索方面的实用性，在与临床专家的一项研究中表现优于著名的PubMed搜索。
The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms -- a fundamental concept across the sciences encompassing activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts.
