Joint Knowledge Base Embedding with Neighborhood Context

Knowledge graph embedding significantly promotes the performance of link prediction and knowledge reasoning, which aims to encode both entities and relations into a low-dimensional semantic space. Existing translation-based methods have achieved state-of-art performances. However, the diversity of connectivity patterns observed in knowledge graph, i.e., structural equivalences, may not be effectively utilized to enhance knowledge graph embedding. To address this issue, we propose a concise but effective model, Context-enhanced Knowledge Graph Embedding (CKGE), for joint knowledge base embedding with neighborhood context. Neighborhood context obtained in our approach gain a deep insight into the diversity of connectivity patterns of knowledge graph. We incorporate the rich structural information contained in neighborhood context to expand the semantic structure of the knowledge graph, which is enable to model complex relations more precisely. We conduct extensive experiments on link prediction, triplet classification on bench-mark datasets. The experiment results show CKGE achieve significant improvements against the baseline methods.