Co-training Embeddings of Knowledge Graphs and Entity Descriptionsfor Cross-lingual Entity Alignment
Table 4: Results of KG completion.
['Language Predict', 'Fr Tail', 'Fr Tail', 'Fr Head', 'Fr Head', 'De Tail', 'De Tail', 'De Head', 'De Head']
Results. KDCoE-mono performs at least comparably to TransE, which indicates that KDCoE preserves well the characterization of monolingual KG structures. Meanwhile, results of cross-lingual prediction prove feasibility of this new method by offering noticeably better outcomes than monolingual prediction. Although this experiment is relatively simple, and may subject to the adequacy of knowledge in the intermediary language, this method opens up a new direction of future work for this task.