The nodes in the commonsense knowledge graph (CSKG) are normally represented by free-form short text (e.g., word or phrase). Different nodes may represent the same concept. This leads to the problems of edge sparsity and node redundancy, which challenges CSKG representation and completion. On the one hand, edge sparsity limits the performance of graph representation learning; On the other hand, node redundancy makes different nodes corresponding to the same concept have inconsistent relations with other nodes. To address the two problems, we propose a new CSKG completion framework based on Contrastive Pretraining and Node Clustering (CPNC). Contrastive Pretraining constructs positive and negative head-tail node pairs on CSKG and utilizes contrastive learning to obtain better semantic node representation. Node Clustering aggregates nodes with the same concept into a latent concept, assisting the task of CSKG completion. We evaluate our CPNC approach on two CSKG completion benchmarks (CN-100K and ATOMIC), where CPNC outperforms the state-of-the-art methods. Extensive experiments demonstrate that both Contrastive Pretraining and Node Clustering can significantly improve the performance of CSKG completion. The source code of CPNC is publicly available on \url{https://github.com/NUSTM/CPNC}.

翻译：常识知识图谱（CSKG）中的节点通常由自由形式的短文本（如单词或短语）表示，不同节点可能指向同一概念。这导致边稀疏性和节点冗余问题，对CSKG表示与补全构成挑战。一方面，边稀疏性限制了图表示学习的性能；另一方面，节点冗余使得对应同一概念的不同节点与其他节点存在不一致的关系。为解决这两个问题，我们提出了一种基于对比预训练与节点聚类（CPNC）的新型CSKG补全框架。对比预训练在CSKG上构建正负头尾节点对，利用对比学习获得更优的语义节点表示；节点聚类将具有相同概念的节点聚合为潜在概念，辅助CSKG补全任务。我们在两个CSKG补全基准（CN-100K和ATOMIC）上评估了CPNC方法，结果表明CPNC优于现有最先进方法。大量实验证明，对比预训练与节点聚类均能显著提升CSKG补全性能。CPNC的源代码已公开发布在\url{https://github.com/NUSTM/CPNC}。