Jointly Learning Entity and Relation Representations for Entity Alignment
Table 1: Summary of the DBP15K datasets.
['[BOLD] DBP15K', '[BOLD] DBP15K', '[BOLD] #Ent.', '[BOLD] #Rel.', '[BOLD] #Rel tr.', '[BOLD] Alignments', '[BOLD] Alignments']
We use DBP15K datasets from sun2017cross to evaluate our approach. DBP15K contains three cross-lingual datasets that were built from the English version to Chinese, Japanese and French versions of DBpedia. Each contains data from two KGs in different languages and provides 15K pre-aligned entity pairs. Besides, each dataset also provides some pre-aligned relations. We manually aligned more relations from the three datasets and removed the ambiguously aligned relation pairs to construct the test sets for relation alignment. We stress that our approach achieves entity and relation alignments simultaneously using only a small number of pre-aligned entities, and relation alignments are only used for testing. Following the previous works sun2017cross; D18-1032; ijcai2018-611, we use 30% of the pre-aligned entity pairs as training data and 70% for testing.