Node-based Knowledge Graph Contrastive Learning for Medical Relationship
  Prediction
Zhiguang Fan Yuedong Yang Mingyuan Xu Hongming Chen
The embedding of Biomedical Knowledge Graphs (BKGs) generates robust
representations, valuable for a variety of artificial intelligence
applications, including predicting drug combinations and reasoning disease-drug
relationships. Meanwhile, contrastive learning (CL) is widely employed to
enhance the distinctiveness of these representations. However, constructing
suitable contrastive pairs for CL, especially within Knowledge Graphs (KGs),
has been challenging. In this paper, we proposed a novel node-based contrastive
learning method for knowledge graph embedding, NC-KGE. NC-KGE enhances
knowledge extraction in embeddings and speeds up training convergence by
constructing appropriate contrastive node pairs on KGs. This scheme can be
easily integrated with other knowledge graph embedding (KGE) methods. For
downstream task such as biochemical relationship prediction, we have
incorporated a relation-aware attention mechanism into NC-KGE, focusing on the
semantic relationships and node interactions. Extensive experiments show that
NC-KGE performs competitively with state-of-the-art models on public datasets
like FB15k-237 and WN18RR. Particularly in biomedical relationship prediction
tasks, NC-KGE outperforms all baselines on datasets such as PharmKG8k-28,
DRKG17k-21, and BioKG72k-14, especially in predicting drug combination
relationships. We release our code at https://github.com/zhi520/NC-KGE.