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.

翻译：我们展示了神经知识图嵌入、精细谷物实体类型预测和神经语言模型的互补性质。 我们展示了由语言模型启发的知识图嵌入方法既能改进知识图嵌入,又能改进精细谷物实体类型表述。 我们的工作还表明,结构化知识图和语言的联合模型都改善了两者。