  We demonstrate the complementary natures of neural knowledge graph embedding,
fine-grain entity type prediction, and neural language modeling. We show that a
language model-inspired knowledge graph embedding approach yields both improved
knowledge graph embeddings and fine-grain entity type representations. Our work
also shows that jointly modeling both structured knowledge tuples and language
improves both.
