The pandemic of COVID-19 has inspired extensive works across different research fields. Existing literature and knowledge platforms on COVID-19 only focus on collecting papers on biology and medicine, neglecting the interdisciplinary efforts, which hurdles knowledge sharing and research collaborations between fields to address the problem. Studying interdisciplinary researches requires effective paper category classification and efficient cross-domain knowledge extraction and integration. In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains. We design frameworks based on contrastive learning for disciplinary classification, and propose a new academic knowledge graph scheme for entity extraction, relation classification and ontology management in accordance with interdisciplinary researches. Based on Covidia, we also establish knowledge discovery benchmarks for finding COVID-19 research communities and predicting potential links.

翻译：COVID-19疫情激发了不同研究领域的广泛工作。现有的COVID-19文献和知识平台仅关注生物学与医学领域的论文收集，忽视了跨学科研究努力，这阻碍了领域间应对该问题的知识共享与研究合作。开展跨学科研究需要有效的论文类别分类以及高效的跨领域知识抽取与集成。本文提出Covidia——COVID-19跨学科学术知识图谱，以弥合不同领域间COVID-19知识的鸿沟。我们设计了基于对比学习的学科分类框架，并针对跨学科研究提出了一种新的学术知识图谱方案，用于实体抽取、关系分类与本体管理。基于Covidia，我们还建立了用于发现COVID-19研究社区和预测潜在关联的知识发现基准。