Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
Table 3: Results on DBP15K w.r.t. k values
['Methods', 'DBPZH-EN H@1', 'DBPZH-EN H@10', 'DBPZH-EN MRR', 'DBPJA-EN H@1', 'DBPJA-EN H@10', 'DBPJA-EN MRR', 'DBPFR-EN H@1', 'DBPFR-EN H@10', 'DBPFR-EN MRR']
AliNet with 2 layers achieves the best performance over all the three metrics. We observe that when AliNet has more layers, its performance declines as well. Although more layers allow AliNet to indirectly capture more distant neighborhood information by layer-to-layer propagation, such distant neighbors would introduce much noise and lead to more non-isomorphic neighborhood structures. We can see that considering two-hop neighborhood leads to the best results. This is similarly attributed to the aforementioned reasons regarding aggregation of multi-hop neighbors. This is further verified by an analysis about DBP15K. For example, in DBPZH-EN, each Chinese entity has 6.6 one-hop neighbors on average and this number for each English entity is 8.6. However, between their one-hop neighbors, there are only 4.5 pairs of counterpart entities, leaving 2.1 Chinese one-hop neighbors and 4.1 English ones unaligned. If considering two-hop neighbors, the numbers of unaligned one-hop neighbors are reduced to 0.5 for Chinese and 0.9 for English, respectively. The numbers have less room to be reduced by introducing more distant neighbors. This suggests us that aggregating two-hop neighborhood information is enough.