Multilingual Knowledge Graph Embeddings forCross-lingual Knowledge Alignment
Table 13: Accuracy of triple-wise alignment verification (%).
['Languages Metric', 'En-Fr Hits@10', 'En-Fr Mean', 'Fr-En Hits@10', 'Fr-En Mean', 'En-De Hits@10', 'En-De Mean', 'De-En Hits@10', 'De-En Mean']
Results of Cross-lingual Entity Matching. For the baselines, LM and CCA are again left far behind for being disjointedly trained. OT, however, takes the position ahead of Var1. This is likely because the knowledge graphs in CN3l are highly sparse, therefore fewer interference of monolingual relations among entities makes the orthogonality constraint easier to fulfill. Even so, in all settings, OT is still largely outperformed by Var4 and Var5, which receives amazingly good results, thus steadily being the optimal solutions. Interestingly, Var3 now ranks right behind the linear-transformation-based variants in most settings. This is quite reasonable because the cross-lingual transitions, which are regarded as a type of relation by Var3 in the graphs, are now way less frequent in CN3l than they are in the much denser and more heterogeneous WK3l. Thus, this explains why it performs better than Var1. Results of Triple-wise Alignment Verification. While Var1, despite being the simplest, takes the second place with a satisfying accuracy in triple-wise alignment verification as well. The relation-dedicated calibration still causes interference in the optimization process of Var2, therefore leads to a 4%∼9% drop of accuracy from Var1. Var3 performs slightly better than Var3. On triple-wise alignment verification on CN3l, we receive exactly the same placement for evaluating the five MTransE variants. Meanwhile, for the baselines, OT is slightly worse than Var1, CCA also receives acceptable accuracy which is however worse than all MTransE variants, while the accuracy of LM is just slightly better than random guessing.