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Contrastive zero-shot relational learning for knowledge graph completion
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AI
DOI：10.1016/j.knosys.2025.113425.png)
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Abstract
En 中文
Knowledge graph completion (KGC) involves enhancing existing factual knowledge by automatically inferring missing links between entities. However, they are limited to inferring relations for entity-pairs that have corresponding samples in the graph. In this paper, we endeavor to identify relations without training examples. To this end, we learn relation embeddings from textual descriptions and utilize a conditional variational autoencoder (C-VAE) to connect these descriptions with the corresponding entity-pair embeddings. However, two problems still persist: first, the embeddings of different relations are entangled, leading to the inability to discriminate between different relations; second, entity-pair embeddings are contaminated with impurities that decrease the accuracy of predictions for novel relations. This study introduces contrastive learning for zero-shot relational learning (CZRL). To better distinguish between different relations, we train a feature encoder with specifically designed contrastive losses. To eliminate noise, we propose a contrastive denoising autoencoder module to isolate the relevant information of entity-pair embeddings from irrelevant information. On two public datasets - Wiki, and NELL - the proposed model demonstrates the performance improved at least 10% compared to baseline models based on evaluation metrics such as MRR, Hit@1, and Hit@5.
Keywords:
Knowledge graph completion
Zero-shot learning
Contrastive learning
Denoising autoencoder
Journal
K
IF:
7.6
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1.2W
Citations:
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