Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment
Table 1: Dataset summary.
['[BOLD] Datasets DBP15K [ITALIC] ZH− [ITALIC] EN', '[BOLD] Datasets Chinese', '[BOLD] Entities 66,469', '[BOLD] Relations 2,830', '[BOLD] Triples 153,929']
These datasets are built upon Chinese, English, Japanese and French versions of DBpedia Each dataset contains 15,000 inter-language links connecting equivalent entities in two KGs of different languages. We use the same training/testing split as previous works, 30% for training, and 70% for testing. Evaluation Metrics. Like previous works, we use Hits@1 to evaluate our model, where a Hits@1 score (higher is better) is computed by measuring the proportion of correctly aligned entities ranked in the top one. Comparison Models. MTransE Model Variants. To evaluate different reasoning methods, we provide three implementation variants of our model for ablation studies, including (1) X-EHD: the baseline model X that only uses our proposed Easy-to-Hard Decoding strategy; (2) X-JEA: the baseline model X that only uses our proposed Joint Entity Alignment method; (3) X-EHD-JEA: the baseline model X that uses both of these two reasoning methods. Implementation details. For the configurations of the alignment model, we use the same settings as Xu et al. The learning rate is set to 0.001. The hop size of two GCN layers is set to 2 and 3, respectively. Following Wu et al. For all datasets, we first use the baseline model to retrieve the top 10 alignments, normalize their scores as probabilities and then perform the proposed coordinated reasoning methods over them. For the Easy-to-Hard decoding method, α is set to 0.75, and K is set to 20. For the joint entity alignment, τ is set to 0.10. For training the enhanced alignment model, for each topic graph pair, we randomly choose at most two gold alignments from the ground truth as the simulated easy alignments.