Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
Table 1: Evaluation results on the datasets.
['Method', '[ITALIC] ZH- [ITALIC] EN @1', '[ITALIC] ZH- [ITALIC] EN @10', '[ITALIC] EN- [ITALIC] ZH @1', '[ITALIC] EN- [ITALIC] ZH @10', '[ITALIC] JA- [ITALIC] EN @1', '[ITALIC] JA- [ITALIC] EN @10', '[ITALIC] EN- [ITALIC] JA @1', '[ITALIC] EN- [ITALIC] JA @10', '[ITALIC] FR- [ITALIC] EN @1', '[ITALIC] FR- [ITALIC] EN @10', '[ITALIC] EN- [ITALIC] FR @1', '[ITALIC] EN- [ITALIC] FR @10']
Following previous works, we used Hits@1 and Hits@10 to evaluate our model, where Hits@k measures the proportion of correctly aligned entities ranked in the top k. Let us first look at the impacts of hop size of GCN2 to our model. In experiments, we find the model achieves the best performance when λ = 3. To better understand on which type of entities that our model could better deal with due to introducing the graph matching layer, we analyze the entities that our model correctly predicts while NodeMatching does not. We find the graph matching layer enhances the ability of our model in handling the entities whose most neighbors in two KGs are different. For such entities, although most local matching information indicate that these two entities are irrelevant, the graph matching layer could alleviate this by propagating the most relevant local matching information throughout the graph.