Learning knowledge graph embeddings by deep relational roto-reflection

Abstract

Embedding methods map entities and relations to low-dimensional vectors and then use a scoring function to predict missing links for knowledge graph completion. Most of the existing deep embedding methods are primarily based on planner convolution, which generates less expressive feature maps that limit the predictive performance. In this paper, we propose a group convolution and hypernetwork-based neural embedding model named, DeepER for knowledge graph completion. DeepER utilizes rotation and reflection transformations of group convolution to produce more expressive feature maps for entities and relations. Furthermore, it introduces a relation-specific roto-reflection of head entities via hypernetwork architecture to preserve the relation-specific information in the embeddings while keeping the rotation and reflection properties of the relations. In addition, we introduce a multimodal extension of DeepER that includes visual and structured information in the embedding vectors of entities. Experimental results demonstrate that DeepER outperforms 20 existing methods on knowledge graph completion benchmarks, consisting of both structured and multimodal datasets.