Confidence-Aware Negative Sampling Method for Noisy Knowledge Graph Embedding

Knowledge graph embedding (KGE) can benefit a variety of downstream tasks, such as link prediction and relation extraction, and has therefore quickly gained much attention. However, most conventional embedding models assume that all triple facts share the same confidence without any noise, which is inappropriate. In fact, many noises and conflicts can be brought into a knowledge graph (KG) because of both the automatic construction process and data quality problems. Fortunately, the novel confidence-aware knowledge representation learning (CKRL) framework was proposed, to incorporate triple confidence into translation-based models for KGE. Though effective at detecting noises, with uniform negative sampling methods, and a harsh triple quality function, CKRL could easily cause zero loss problems and false detection issues. To address these problems, we introduce the concept of negative triple confidence and propose a confidence-aware negative sampling method to support the training of CKRL in noisy KGs. We evaluate our model on the knowledge graph completion task. Experimental results demonstrate that the idea of introducing negative triple confidence can greatly facilitate performance improvement in this task, which confirms the capability of our model in noisy knowledge representation learning (NKRL).