Cross-lingual Entity Alignment for Knowledge Graphs with Incidental Supervision from Free Text
Table 5: Vocabulary coverage and average match per entity on the five language-specific Wikipedia corpora.
['Language Estimation', 'SFM Coverage', 'SFM Avg match', 'EDL Coverage', 'EDL Avg match']
Due to the lack of ground truths on unlabeled text, it is hard to estimate the precision of entity grounding by the two types of techniques. However, as the requirement of the grounding process is to simply connect two data modalities for training embeddings, we may encourage a technique that handles enough entity mentions and offer a higher coverage on entity vocabularies. As we can observe that, without considering disambiguation, SFM can overall cover higher proportions of the entity vocabularies, while pre-trained EDL generally discovers more entity mentions for each entity. However, both techniques are sufficient to support the noisy grounding process and combine two data modalities for embedding learning and alignment induction.