Rule-Guided Compositional Representation Learning on Knowledge Graphs
Table 6: Entity prediction results on FB15K by mapping properties of relations (%) with filtered setting.
['Models', 'Head Prediction (Hits@10) 1-1', 'Head Prediction (Hits@10) 1-N', 'Head Prediction (Hits@10) N-1', 'Head Prediction (Hits@10) N-N', 'Tail Prediction (Hits@10) 1-1', 'Tail Prediction (Hits@10) 1-N', 'Tail Prediction (Hits@10) N-1', 'Tail Prediction (Hits@10) N-N']
We can observe that: 1) RPJE outperforms all baselines significantly and consistently in regard to all the relation categories. Compared to the best performing baseline TransG, RPJE achieves an average improvement of 4.2% in head entities prediction and 6.5% in tail entities prediction. 2) More interestingly, on the two toughest tasks of predicting head entities of N-1 relation and predicting tail entities of 1-N relation, our approach achieves the best performance improvements approximately 12.6% and 13.4% compared to the best baselines, respectively.