Aligning Cross-Lingual Entities with Multi-Aspect Information
Table 3: Results of using both graph and textual information on DBP15K and DBP100K. @1, @10, and @50 refer to Hits@1, Hits@10, and Hits@50, respectively. ∗ indicates results are taken from Sun et al. (2017).
['Model', 'ZH → EN', 'EN→ ZH', 'JA → EN', 'EN→ JA', 'FR → EN', 'EN→ FR']
Applying literal descriptions of entities to conduct cross-lingual entity alignment is relatively under-explored. The recent work of Chen et al. We directly take results reported by Sun et al. The PairwiseBert model learns the textual embeddings that map cross-lingual descriptions into the same space, which can be directly used to align entities. We can see that it achieves good results on its own, which also shows the efficacy of using multilingual descriptions. The results are reported as Man (Weighted) and Hman (Weighted), respectively. As we can see, this simple operation leads to significant improvements and gives excellent results across all datasets. However, it is not always the case that KGs provide descriptions for every entity. For those entities whose descriptions are not available, the graph embeddings would be the only source for making alignment decisions.