Commonsense Knowledge Graph Completion Method Based on Relation-Constrained Contrastive Learning

Knowledge graph completion aims to address the problems of knowledge deficiency and incompleteness, by predicting missing entities or relationships in a knowledge graph. Compared to traditional knowledge graphs, commonsense knowledge graphs are typically sparser, making them insufficient for representing entities solely based on structural information. Therefore, some studies enrich commonsense knowledge graphs by utilizing semantic representations in addition to structural information. However, these methods typically focus only on the semantic representation of individual entities and ignore the semantic associations between entity sets. To address this issue, this study proposes a new method called relation-constrained contrastive learning for common-sense knowledge graph completion. First, the method uses relations to divide entities into different sets and selects positive and negative sample pairs from these sets for contrastive learning, to obtain the basic representations of the entities. It further learns comprehensive entity representations by constraining the similarity between individual entity semantic representations and the central representations of the sets to which the entities belong. The completion task is performed based on these comprehensive representations. Experiments on two public datasets show that the proposed model outperforms baseline models. Compared to the second-best model, CPNC, the proposed model improves the Mean Reciprocal Rank (MRR) by 1.09 and 2.48 percentage points and Hits@1 by 1.02 and 1.55 percentage points on the CN-100K and ATOMIC datasets, respectively.