A Joint Framework for Inductive Representation Learning and Explainable Reasoning in Knowledge Graphs
Table 5: The MRR for the test triples in inductive setting with to-Many and to-1 relation types. The % columns show the percentage of test triples for each relation type.
['Dataset', 'to-Many %', 'to-Many MRR', 'to-1 %', 'to-1 MRR']
Following \newciteBordes:2013, we categorize the relations in the seen snapshot of the knowledge graph into Many-to-1 and 1-to-Many relations. The categorization is done based on the ratio of the cardinality of the target answer entities to the source entities. If the ratio is greater than 1.5, we categorize the relation as to-Many, otherwise it is categorized as to-1. We analysed the results of the test set for these two types of relations. For FB15k-237 and NELL-995, our model performs better for to-1 relations than to-many relations. On the contrary, we observe a reverse trend for the WN18RR dataset. Note however that to-many relations have alternative target entities. In the current evaluation protocol, our model is punished for predicting any alternative target entity other than the ground truth target.