Multilingual Knowledge Graph Embeddings forCross-lingual Knowledge Alignment
Table 4: Cross-lingual entity matching result.
['Data Set Aligned Languages', 'WK3l-15k En-Fr', 'WK3l-15k En-Fr', 'WK3l-15k Fr-En', 'WK3l-15k Fr-En', 'WK3l-15k En-De', 'WK3l-15k En-De', 'WK3l-15k De-En', 'WK3l-15k De-En', 'WK3l-120k En-Fr', 'WK3l-120k Fr-En', 'WK3l-120k En-De', 'WK3l-120k De-En']
Results. As expected, without jointly adapting the monolingual vector spaces with the knowledge alignment, LM and CCA are largely outperformed by the rest. While the orthogonality constraint being too strong to be enforced in these cases, OT performs at most closely to the simplest cases of MTransE. For MTransE , Var4 and Var5 outperform the other three variants under all settings. The fairly close results obtained by these two variants indicate that the interference caused by learning an additional relation-dedicated transformation in Var5 is negligible to the entity-dedicated transformation. Correspondingly, we believe that the reason for Var3 to be outperformed by Var4 and Var5 is that it fails to differentiate well the over-frequent cross-lingual alignment from regular relations. Therefore, the characterization for cross-lingual alignment is negatively affected by the learning process for monolingual relations in a visible degree. Axis calibration appears to be unstable on this task. We hypothesize that this simple technique is affected by two factors: coherence between language-specific versions, and density of the graphs. Var2 is always outperformed by Var1 due to the negative effect of the calibration based on relations. Although Var1 still outperforms Var3 on entity matching between English and French graphs in WK3l-15k, coherence somewhat drops alongside when scaling up to the larger data set so as to hinder the calibration. The German graphs are sparse, thus should have set a barrier for precisely constructing embedding vectors and hindered calibration on the other side. Therefore Var1 still performs closely to Var3 in the English-German task on WK3l-15k and English-French task on WK3l-120k, but is outperformed by Var3 in the last setting. In general, the variants that use linear transformations are the most desired.