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

Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering