Go for a Walk and Arrive at the Answer:Reasoning Over Paths in Knowledge Basesusing Reinforcement Learning
Table 3: Query answering results on Kinship and UMLS datasets.
['[BOLD] Data', '[BOLD] Metric', '[BOLD] ComplEx', '[BOLD] ConvE', '[BOLD] DistMult', '[BOLD] NTP', '[BOLD] NTP- [ITALIC] λ', '[BOLD] NeuralLP', '[BOLD] MINERVA']
The Kinship and UMLS datasets are small KB datasets with around 100 entities each On Kinship, minerva outperforms both NeuralLP and NTP and matches the hits@10 performance of NTP on UMLS. Unlike countries, these datasets were not designed to test the logical rule learning ability of models and given the small size, embedding based models are able to get really high performance. Combination of both methods gives a slight increase in performance as can be seen from the results of NTP-λ. However, when we initialized minerva with pre-trained embeddings of ComplEx, we did not find a significant increase in performance.