Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph
This addresses the need for better collaboration among researchers in different fields studying COVID-19, though it is incremental as it builds on existing knowledge graph methods.
The authors tackled the problem of limited interdisciplinary knowledge sharing in COVID-19 research by creating Covidia, an academic knowledge graph that integrates papers across domains, resulting in benchmarks for community detection and link prediction.
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.