Co-training Embeddings of Knowledge Graphs and Entity Descriptionsfor Cross-lingual Entity Alignment
Table 2: Results of cross-lingual entity alignment.
['Language Metric', 'En-Fr  [ITALIC] Hit@1', 'En-Fr  [ITALIC] Hit@10', 'En-Fr  [ITALIC] MRR', 'En-De  [ITALIC] Hit@1', 'En-De  [ITALIC] Hit@10', 'En-De  [ITALIC] MRR']
Results. i2−i4) and KDCoE (term)). Among all baselines, MTransE-LT notably outperforms others, including other MTransE variants. The orthogonality constraint of OT seems to be too strict so that it impairs the performance. Without jointly adapting the monolingual vector spaces with the alignment, off-line approaches LM and CCA are left behind. On both language settings, KDCoE is able to gradually improve MTransE-LT in every iteration of co-training. The most significant improvements happen in the first iterations, where a majority of candidate ILLs are to be proposed. The final stages of KDCoE (6th and 5th iterations of the two settings) outperform the best baseline by almost doubling Hit@1 as well as offering significantly higher Hit@10 and MRR. Hence, the co-training approach of KDCoE on enhancing semi-supervised entity alignment is very promising.