Path Ranking with Attention to Type Hierarchies
Table 5: MAP% comparison to two SOTA embedding methods.
['[EMPTY]', 'WN18RR', 'FB15k-237']
In fact, all neural-based path ranking methods (APR and Path-RNN) outperform the KGE methods. One possible reason that path ranking methods outperform KGE is that KGE methods require both the source ei and target entities ek in the missing triple (ei,rj,ek) to be known, thus failing to perform well when test entities are not present in the training set. However, only 17.6039% of FB15k-237’s test data consists of new entities, and only 0.9412% in WN18RR. As a result, new entities do not alone account for the difference in performance. This comparison additionally affirms the improved state-of-the-art performance in fact prediction achieved by our method.