  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.
