Modeling Relation Paths for Representation Learning of Knowledge Bases
Table 2: Evaluation results on entity prediction.
['Metric', 'Mean Rank Raw', 'Mean Rank Filter', 'Hits@10 (%) Raw', 'Hits@10 (%) Filter']
(1) PTransE significantly and consistently outperforms other baselines including TransE. It indicates that relation paths provide a good supplement for representation learning of KBs, which have been successfully encoded by PTransE. For example, since both George W. Bush and Abraham Lincoln were presidents of the United States, they exhibit similar embeddings in TransE. This may lead to confusion for TransE to predict the spouse of Laura Bush. In contrast, since PTransE models relation paths, it can take advantage of the relation paths between George W. Bush and Laura Bush, and leads to more accurate prediction. (2) For PTransE, the addition operation outperforms other composition operations in both Mean Rank and Hits@10. The reason is that, the addition operation is compatible with the learning objectives of both TransE and PTransE. Take hr1−→e1r2−→t for example. The optimization objectives of two direct relations h+r1=e1 and e1+r2= t can easily derive the path objective h+r1+r2=t. (3) PTransE of considering relation paths with at most 2-step and 3-step achieve comparable results. This indicates that it may be unnecessary to consider those relation paths that are too long.