Locally Adaptive Translation for Knowledge Graph Embedding
Table 3: Evaluation results on link prediction.
['Data sets Metric', 'WN18 Mean Rank', 'WN18 Mean Rank', 'FB15K Mean Rank', 'FB15K Mean Rank']
All parameters are determined on the validation set. It can be seen that on both data sets, TransA obtains the lowest mean rank. Furthermore, on WN18, among the baselines, Unstructured and TransH(unif) perform the best, but TransA decreases the mean rank by about 150 compared with both of them. On FB15K, among the baselines, TransH(unif) is the best baseline. TransA decreases its mean rank by 30∼50. Notice that the decreases on WN18 and FB15K are different, because the number of relations in WN18 is quite small and the relation-specific margin is very small too. In this case, the optimal margin is almost equal to the entity-specific margin. While on FB15K, the number of relations is 1345, and the optimal margin is the combination of the entity-specific margin and the relation-specific margin.