Modeling Relation Paths for Representation Learning of Knowledge Bases
Table 3: Evaluation results on FB15K by mapping properties of relations. (%)
['Tasks Relation Category', 'Predicting Head Entities (Hits@10) 1-to-1', 'Predicting Head Entities (Hits@10) 1-to-N', 'Predicting Head Entities (Hits@10) N-to-1', 'Predicting Head Entities (Hits@10) N-to-N', 'Predicting Tail Entities (Hits@10) 1-to-1', 'Predicting Tail Entities (Hits@10) 1-to-N', 'Predicting Tail Entities (Hits@10) N-to-1', 'Predicting Tail Entities (Hits@10) N-to-N']
Here we demonstrate the performance of PTransE It is observed that, on all mapping types of relations, PTransE consistently achieves significant improvement as compared with TransE.