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Register a new userExtracting a Knowledge Base of Mechanisms from COVID-19 Papers
استخراج قاعدة معارف من آليات من ورقات Covid-19
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ولدت جائحة Covid-19 هيئة متنوعة من الأدبيات العلمية تحديا في التنقل، وتحفيز الاهتمام بالأدوات الآلية للمساعدة في العثور على معرفة مفيدة.نحن نتابع بناء قاعدة المعرفة (KB) من الآليات --- مفهوم أساسي في جميع أنحاء العلوم، والذي يشمل الأنشطة والوظائف والعلاقات السببية، بدءا من العمليات الخلوية إلى الآثار الاقتصادية.استخراج هذه المعلومات من اللغة الطبيعية للأوراق العلمية من خلال تطوير مخطط واسع موحد يضرب التوازن بين الأهمية والاتساع.نبحث عن مجموعة بيانات من الآليات مع مخططنا وتدريب نموذج لاستخراج علاقات الآلية من الأوراق.توضح تجاربنا فائدة KB لدينا في دعم البحث العلمي متعدد التخصصات على أدب CovID-19، مما يتفوق على البحث البارز PubMed في دراسة ذات خبراء سريريين.محرك البحث لدينا، مجموعة البيانات والرمز متاحة للجمهور.
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, which encompasses 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. Our search engine, dataset and code are publicly available.
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