Modeling Relation Paths for Representation Learning of Knowledge Bases
Table 4: Evaluation results on relation prediction.
['Metric', 'Mean Rank Raw', 'Mean Rank Filter', 'Hits@1 (%) Raw', 'Hits@1 (%) Filter']
Since our implementation of TransE has achieved the best performance among all baselines for entity prediction, here we only compare PTransE with TransE due to limited space. In this table, we report the performance of TransE without reverse relations (TransE), with reverse relations (+Rev) and considering relation paths for testing like that in PTransE (+Rev+Path). (2) Even for TransE itself, considering relation paths for testing can reduce 17.3% errors as compared with TransE+Rev. It indicates that encoding relation paths will benefit for predicting relations. (3) PTransE with only considering relation paths (PTransE-TransE) gets surprisingly high mean rank. The reason is that, not all entity pairs in testing triples have relation paths, which will lead to random guess and the expectation of rank of these entity pairs is |R|/2. Meanwhile, Hits@1 of PTransE-TransE is relatively reasonable, which indicates the worth of modeling relation paths. As compared with TransE, the inferior of PTransE-TransE also indicates that entity representations are informative and crucial for relation prediction.