Path Ranking with Attention to Type Hierarchies
Table 2: MAP% of APR (bottom) and baseline methods (top).
['[EMPTY]', 'Data Types', 'Pooling Method', 'WN18RR', 'FB15k-237']
We compared the performance of APR to the following methods All APR variants outperformed the prior state of the art on both datasets. This result indicates that adding types, with the right balance between being generalizable and discriminative, helps create path patterns that allow for more accurate prediction. Our best model APRD, which further leverages attention pooling to reason about the rich contextual information in paths, is able to improve state-of-the-art performance by 26% on WN18RR and 10% on FB15k-237 with (p<0.005). We compared our proposed attention pooling to three existing pooling methods. The superior performance is likely due to the pooling method’s ability to collectively reason about the rich contextual information in paths. The other three methods lose information when they compress path representations to single values.