Entity-Context and Relation-Context Combined Knowledge Graph Embeddings

Hierarchical structures are very common in knowledge graphs, and semantic hierarchy-preserved knowledge graph embeddings have achieved promising results in the knowledge graph link prediction task. However, handling one-to-many, many-to-one, and many-to-many relations that can provide hierarchical information is challenging and brings entity indistinguishability issues. To address this limitation, this paper proposes a novel knowledge graph embedding model, namely Entity-context and Relation-context combined Knowledge Graph Embeddings (ERKE), in which each relation is defined as a rotation with variable moduli from the source entity to the target entity in the polar coordinate system. It can be seen as a combination of two spaces—modulus space and phase space. In the modulus space, modulus information is used to model semantic hierarchies, and entity-context information is adopted to make node representations more expressive. Besides, based on the design of the propagation rule of Graph Convolution Network (GCN), a new GCN model suitable for processing semantic hierarchies in knowledge graphs is proposed. In the phase space, relation-context information is used to make entities easier to distinguish. Specifically, a rotation operation in the polar coordinate system is transformed to the addition operation in the rectangular coordinate system, and relations between entities are mapped into their entity-specific hyperplanes. The proposed method is verified by the experiments on three benchmark datasets, and experimental results demonstrate that the proposed method can learn the semantic hierarchies in knowledge graphs and improve the prediction accuracy of complex one-to-many, many-to-one, and many-to-many cases simultaneously.