Go for a Walk and Arrive at the Answer:Reasoning Over Paths in Knowledge Basesusing Reinforcement Learning
Table 4: Query answering results on wn18rr, fb15k-237 and nell-995 datasets. NeuralLP does not scale to nell-995 and hence the entries are kept blank.
['[BOLD] Data', '[BOLD] Metric', '[BOLD] ComplEx', '[BOLD] ConvE', '[BOLD] DistMult', '[BOLD] NeuralLP', '[BOLD] Path-Baseline', '[BOLD] MINERVA']
We could not include the results of NeuralLP on nell-995 since it didn’t scale to that size. Similarly NTP did not scale to any of the larger datasets. Apart from these, we are the first to report a comprehensive summary of performance of all baseline methods on these datasets. As we can see minerva outperforms this baseline significantly. This shows that a model which predicts based on a set of randomly sampled paths does not do as well as minerva because it either loses important paths during random walking or it fails to aggregate predictive features from all the k paths, many of which would be irrelevant to answer the given query. The latter is akin to the problem with distant supervision However, by taking each step conditioned on the query relation, minerva can effectively reduce the search space and focus on paths relevant to answer the query.