TransHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction. (arXiv:2204.13221v1 [cs.AI])

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 …

ai arxiv embedding graph knowledge knowledge graph