Aligning Cross-Lingual Entities with Multi-Aspect Information
Table 2: Results of using graph information on DBP15K and DBP100K. @1, @10 and @50 refer to Hits@1, Hits@10 and Hits@50, respectively.
['Model', 'ZH → EN', 'EN→ ZH', 'JA → EN', 'EN→ JA', 'FR → EN', 'EN→ FR']
We first compare Man and Hman against previous systems Hao et al. ; Sun et al. It is worth noting that, in this case, Man and Hman use as much information as Wang et al. The performance improvements confirm that our model can better utilize topological, relational, and attribute information of entities provided by KGs. Moreover, we perform ablation studies on the two proposed models to investigate the effectiveness of each component. We alternatively remove each aspect of features (i.e., topological, relation, and attribute features) and the highway layer in Hman, denoted as w/o te (re, ae, and hw). These figures prove that these two aspects of features are useful in making alignment decisions. On the other hand, compared to Man, Hman shows more significant performance drops, which also demonstrates that employing the feedforward networks can better categorize relation and attribute features than GCNs in this scenario. Interestingly, looking at the two variants Man w/o te and Hman w/o te, we can see the former achieves better results. Since Man propagates relation and attribute features via graph structures, it can still implicitly capture topological knowledge of entities even after we remove the topological features. However, Hman loses such structure knowledge when topological features are excluded, and thus its results are worse. From these experiments, we can conclude that the topological information is playing an indispensable role in making alignment decisions.