Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence
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Knowledge graphs (KGs) can provide significant relational information and have been widely utilized in various tasks. However, there may exist amounts of noises and conflicts in KGs, especially in those constructed automatically with less human supervision. To address this problem, we propose a novel confidence-aware knowledge representation learning framework (CKRL), which detects possible noises in KGs while learning knowledge representations with confidence simultaneously. Specifically, we introduce the triple confidence to conventional translation-based methods for knowledge representation learning. To make triple confidence more flexible and universal, we only utilize the internal structural information in KGs, and propose three kinds of triple confidences considering both local triple and global path information. We evaluate our models on knowledge graph noise detection, knowledge graph completion and triple classification. Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of our CKRL model in both noise detection and knowledge representation learning.
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