Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
Table 3: Results of mh-PQA
['[EMPTY]', 'methods', 'MQ', 'H@10']
Results. top baselines on BaseKGP (block 1), ROP results on BaseKGP (block 2) and ROP results on EnhancedKGP (block 3). Overall, our ROP models all get new state-of-the-art by large margins (2–4% H@10 on BaseKGP, 4–6% H@10 on EnhancedKGP). The improvements of the second block over the first are evidence that recurrent neural networks are an appropriate paradigm in this task. The third block shows that encoding intermediate entities in an appropriate way, like ROP does, can give a further boost.