Exploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings
Table 1: Comparison of the performance of various KG Embedding approaches on the FB15k dataset. Source: [33]
['[BOLD] Model', '[BOLD] MRR', '[BOLD] Hits@10']
For KG embeddings, we chose to work with ComplEx because it has been shown to perform well on KGs. On the FB15k dataset, ComplEx was shown to perform better compared to TransE and DistMult on the prediction of relations in the FB15k dataset The resulting ComplEx embedding model represents entities as 400-dimension vectors.