Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
Table 1: Results of mh-KBC task
['Model', 'MAP']
Results. It ignores all information about within-path entities and trains separate models per relation. further improves the result by representing entities with the sum of their type embeddings. Thus, RNN-path-type uses extra information, the entity types. Apart from these baselines, it is also feasible to compare with the baselines based on composition of one-hop triple-based embedding models.