Rule-Guided Compositional Representation Learning on Knowledge Graphs
Table 5: Entity prediction results on FB15K. The missing values indicate the scores not reported in the original work. The best score is in bold, and the second best in underline.
['Models', 'MR raw', 'MR filtered', 'MRR filtered', 'Hits@ 10(%) raw', 'Hits@ 10(%) filtered']
This demonstrates that RPJE learns more reasonable embeddings for KGs via using logic rules in conjunction with paths. (2) In particular, RPJE outperforms PTransE on each metric, which indicates the superiority of introducing logic rules for providing higher accuracy in paths composition and learning better path embeddings. (3) Compared to the rule-based baselines KALE and RUGE, RPJE obtains the improvements of 56.0%/6.3% on MRR and 18.5%/4.4% on Hits@10 (filtered), which demonstrates the effectiveness of explicitly employing rules for preserving more semantic information and further integrating paths.