Incremental Update of Knowledge Graph Embedding by Rotating on Hyperplanes

Knowledge graph embedding (KGE) plays an important role in downstream tasks, such as question answering, recommendation system, and entity recognition. Most existing KGE methods focus on modeling static knowledge graphs. However, many knowledge graphs are incremental in reality. Existing KGE methods are time-consuming to update the embedding space incrementally, and have difficulty in keeping the timeliness of the knowledge graph embedding. To address this problem, we propose a novel knowledge graph embedding method by rotating on hyperplane (RotatH), which supports updating the embedding space incrementally and ensures the timeliness and accuracy of knowledge graph embedding. Specifically, our proposed method first employs relation-specific hyperplanes to update the incremental entities into the trained vector space efficiently. Meanwhile, by combining hyperplane and rotation, our method can deal with complex relations, such as many-to-many and symmetry relations, and has high performance in both incremental and static environments. Moreover, our method introduces a mean-based method to constraint the density of incremental entities. We conduct extensive link prediction experiments on two real-world incremental datasets and two benchmark datasets. The experimental results show that our model incrementally updates embedding space efficiently and outperforms static models on benchmarks.