  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}.
