A confidence-aware and path-enhanced convolutional neural network embedding framework on noisy knowledge graph

In recent years, Knowledge Graphs (KGs) have been widely used in applications such as search engines and Q&A systems. During construction, errors were inevitably introduced and KGs may contain many incorrect facts. However, most KG representation learning methods assume that all triple facts are correct, making KG noise detection a challenge. Although some knowledge representation methods are based on a confidence-aware framework, they are usually limited by the translation assumption in TransE and confidence calculation methods. To address the above problems, we propose a more accurate and effective confidence-aware and path-enhanced convolutional neural network knowledge embedding framework (CPConvKE). It makes use of structural, entity type, and rule information for knowledge graph noise detection and knowledge representation learning simultaneously. Specifically, for representation learning, we introduce a gating-based path embedding method to filter out noise while learning entity and relation embeddings. For the confidence evaluation, we propose a triple confidence estimator that uses entity type and rule information as prior probability and defines posterior probability based on the embedding. We evaluate the effectiveness of our model on the knowledge graph noise detection, completion, and triple classification tasks. The experimental results show that our confidence-aware model achieves significant and consistent improvements on all tasks, which confirms the ability of CPConvKE to detect noise and learn clean embeddings in a noisy knowledge graph.