Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs
Table 2: Evaluation results on link prediction with different relation categories.
['Tasks Relation Category', 'Predicting Head (Hits@10) 1-to-1', 'Predicting Head (Hits@10) 1-to-N', 'Predicting Head (Hits@10) N-to-1', 'Predicting Head (Hits@10) N-to-N', 'Predicting Tail (Hits@10) 1-to-1', 'Predicting Tail (Hits@10) 1-to-N', 'Predicting Tail (Hits@10) N-to-1', 'Predicting Tail (Hits@10) N-to-N']
(2) The improvements locate more in N-to-N relations. It indicates that textual information is important when modeling complex relations, and our STKRL models could well capture the useful textual information for better knowledge representations.