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.

翻译：生物医学知识图谱（BKGs）的嵌入能够生成鲁棒的表示，这对多种人工智能应用（包括预测药物组合和推理疾病-药物关系）具有重要价值。同时，对比学习（CL）被广泛用于增强这些表示的区分度。然而，在知识图谱（KGs）中构造合适的对比对一直是挑战。本文提出了一种新颖的基于节点的知识图谱嵌入对比学习方法NC-KGE。NC-KGE通过在知识图谱上构建合适的对比节点对，增强了嵌入中的知识提取能力，并加速了训练收敛。该方案可以轻松与其他知识图谱嵌入（KGE）方法集成。针对生化关系预测等下游任务，我们在NC-KGE中引入了关系感知注意力机制，专注于语义关系和节点交互。大量实验表明，NC-KGE在FB15k-237和WN18RR等公开数据集上与最先进模型表现相当。特别是在生物医学关系预测任务中，NC-KGE在PharmKG8k-28、DRKG17k-21和BioKG72k-14等数据集上优于所有基线模型，尤其在预测药物组合关系方面表现突出。我们已开源代码：https://github.com/zhi520/NC-KGE。