Path Ranking with Attention to Type Hierarchies
Table 3: MAP% of three models using different levels of abstraction for entities.
['[EMPTY]', 'WN18RR', 'FB15k-237']
We further investigated whether our model learns the proposed path patterns that balance between discrimination and generalization. For comparison, we modified the best model APRD by fixing the selection of types to either the most specific level or the most abstract level. Using a fixed level of abstraction leads to worse performance compared to using attention. This result not only confirms that balancing between generalization and discrimination makes prediction more accurate, but also that our proposed model achieves this balance by learning the correct levels of abstraction for entities. We also examined the distribution of attention weight at different levels of type hierarchies. More specifically, level 1, 2, and 3 are most commonly emphasized for entities in WN18RR; level 1,2, and 6 are most often used for entities in FB15k-237. However, the diversity of levels used by the model proves that not all entities should use the most specific level: different entities require different levels of abstraction.