Abstract
Most knowledge graphs (KGs) are incomplete in the real world, so knowledge graph completion (KGC) is widely investigated to predict the most credible missing facts from given knowledge. However, existing KGC methods heavily rely on the given facts to predict missing relations between entities, ignoring the value of external knowledge. In addition, previous knowledge representation methods ignore the multi-perspective characteristics of cognate knowledge, which leds to the inability to obtain high-level semantic representation of knowledge. To alleviate the above issues, this paper proposes a Commonsense Aware Contrastive Learning (CACL) framework, which extracts relevant knowledge triples from existing commonsense knowledge base to assist in the KGC. Moreover, our method employs knowledge contrast representation learning method to acquire the higher-order representation from multiple perspectives. Experiments show that our method improves the performance of basic knowledge graph embedding (KGE) models. Our method also could be easily adapted to various KGE models.
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Dong, C., Xu, F., Wang, Y., Liu, P., Xu, L. (2024). CACL:Commonsense-Aware Contrastive Learning for Knowledge Graph Completion. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1968. Springer, Singapore. https://doi.org/10.1007/978-981-99-8181-6_37
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