Go for a Walk and Arrive at the Answer:Reasoning Over Paths in Knowledge Basesusing Reinforcement Learning
Table 2: Performance on three tasks of countries dataset with AUC-PR metric. minerva significantly outperforms all other methods on the hardest task (S3). Also variance across runs for minerva is lower compared to other methods.
['[EMPTY]', '[BOLD] ComplEx', '[BOLD] ConvE', '[BOLD] DistMult', '[BOLD] NTP', '[BOLD] NTP- [ITALIC] λ', '[BOLD] NeuralLP', '[BOLD] MINERVA']
Dataset The countries dataset ontains countries, regions, and subregions as entities and is carefully designed to explicitly test the logical rule learning and reasoning capabilities of link prediction models. The queries are of the form LocatedIn(c, ?) and the answer is a region (e.g. LocatedIn(Egypt, ?) with the answer as Africa). Following the design of the countries dataset, for task S1 and S2, we set the maximum path length T=2 and for S3, we set T=3. The Unified Medical Language System (UMLS) dataset, is from biomedicine. The entities are biomedical concepts (e.g. disease, antibiotic) and relations are like treats and diagnoses. The Kinship dataset contains kinship relationships among members of the Alyawarra tribe from Central Australia. For these two task we use maximum path length T=2. We can see that minerva compares favorably or outperforms all the baseline models except on the task S2 of countries, where the ensemble model ntp-λ and ConvE outperforms it, albeit with a higher variance across runs. Our gains are much more prominent in task S3, which is the hardest among all the tasks.