MDistMult: A Multiple Scoring Functions Model for Link Prediction on Antiviral Drugs Knowledge Graph

TL;DR

This paper introduces MDistMult, a novel model using multiple scoring functions to improve link prediction accuracy on COVID-19 knowledge graphs, addressing the limitations of single-function models.

Contribution

The paper proposes MDistMult, a new approach that leverages multiple scoring functions for better feature extraction in link prediction tasks on COVID-19 KGs.

Findings

MDistMult achieves state-of-the-art performance on CADKG.

Using multiple scoring functions improves link prediction accuracy.

The model effectively captures richer features of COVID-19 knowledge graphs.

Abstract

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