  Knowledge graphs (KGs) on COVID-19 have been constructed to accelerate the
research process of COVID-19. However, KGs are always incomplete, especially
the new constructed COVID-19 KGs. Link prediction task aims to predict missing
entities for (e, r, t) or (h, r, e), where h and t are certain entities, e is
an entity that needs to be predicted and r is a relation. This task also has
the potential to solve COVID-19 related KGs' incomplete problem. Although
various knowledge graph embedding (KGE) approaches have been proposed to the
link prediction task, these existing methods suffer from the limitation of
using a single scoring function, which fails to capture rich features of
COVID-19 KGs. In this work, we propose the MDistMult model that leverages
multiple scoring functions to extract more features from existing triples. We
employ experiments on the CCKS2020 COVID-19 Antiviral Drugs Knowledge Graph
(CADKG). The experimental results demonstrate that our MDistMult achieves
state-of-the-art performance in link prediction task on the CADKG dataset
