Compositional Approaches for Representing Relations Between Words: A Comparative Study
Table 4: Accuracy of the compositional operators for knowledge base completion task.
['Compositional operator', 'WN18 MeanRank', 'WN18 Hits@10(%)', 'FB15k MeanRank', 'FB15k Hits@10(%)']
As can be seen from the Table, Mult operator yields the lowest mean rank and the highest Hits@10 accuracy among other operators for the both knowledge bases. On the other hand, PairDiff operator scores test entity pairs by (→h−→t)\T(→h′−→t′). Here, (h′,t′) is an entity pair in the test dataset with the target relation r, and we are interested in finding, for example , candidate tail entities t′ that has r with a given head entity h′. This score can be further expanded as (→h−→t)\T→h′−(→h−→t)\T→t′. The first term is fixed given the training dataset and the head entity, and the rank of the tail entity is determined purely based on (→h−→t)\T→t′, where the head entity h′ does not participate in. we see that mean rank for PairDiff is significantly higher compared to that of Mult. This suggests that many irrelevant tail (or head) entities are ranked ahead of the correct entity for each test tuple. On the other hand, in relational similarity task, the two pairs between which we must measure similarity are fixed and this issue is not