基于三维空间旋转平移的自适应知识表示方法.

The existing knowledge graph representation learning studies generally suffer from the problems of neglecting the semantic space of specific relations, or difficulty in modeling non-injective complex relations, or difficulty in modeling multiple relation patterns, especially poor performance on two relation patterns of non-commutative combinations as well as subrelations. To address this problem, based on the adaptive projection of entities, this paper proposed a new model with strong representation ability, called ATR3DKRL. By extending the rotation operation from 2D to 3D using the Rodrigues' rotation formula with translation optimization, it could be demonstrated through theoretical derivation that the model could model non-injective complex relationships and multiple relation patterns. The experimental results on several generic datasets show that the model can effectively improve link prediction accuracy, leading existing baseline models in four metrics in dataset DB100K and FB15K-237. Comparing to the baseline model RotatE on the evaluation indicators MRR and H@1 in DB100K, it can significantly increase by 3.3% and 6.5%. [ABSTRACT FROM AUTHOR]