Rule-Guided Compositional Representation Learning on Knowledge Graphs
Table 9: Entity prediction results on WN18 and NELL-995.
['Models', 'WN18 MRR', 'WN18 Hits@10(%)', 'NELL-995 MRR', 'NELL-995 Hits@10(%)']
We also test the models on datasets WN18 and NELL-995. Three types of typical models are selected as baselines. Few rules can be mined from WN18 due to extremely limited amount of relations. And very parse paths can be extracted from NELL-995 because entities are far more than relations on this dataset. It illustrates the superiority of our approach for representation learning on various large scale KGs. On the other hand, considering the performance gains on FB15K are more than that on WN18, which is because more rules provide more semantic information to RPJE to use. As can be expected, our model RPJE will obtain better performance on datasets which implies more rules and paths.