TransHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction

Knowledge graph embedding methods are important for knowledge graph completion (link prediction) due to their robust performance and efficiency on large-magnitude datasets. One state-of-the-art method, PairRE, leverages two separate vectors for relations to model complex relations (i.e., 1-to-N, N-to-1, and N-to-N) in knowledge graphs. However, such a method strictly restricts entities on the hyper-ellipsoid surface and thus limits the optimization of entity distribution, which largely hinders the performance of knowledge graph completion. To address this problem, we propose a novel score function TransHER, which leverages relation-specific translations between head and tail entities restricted on separate hyper-ellipsoids. Specifically, given a triplet, our model first maps entities onto two separate hyper-ellipsoids and then conducts a relation-specific translation on one of them. The relation-specific translation provides TransHER with more direct guidance in optimization and the ability to learn semantic characteristics of entities with complex relations. Experimental results show that TransHER can achieve state-of-the-art performance and generalize to datasets in different domains and scales. All our code will be publicly available.