论文标题
关于知识图嵌入，细谷物实体类型和语言建模的互补性质
On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling
论文作者
论文摘要
我们证明了神经知识图，细粒度实体类型预测和神经语言建模的互补性本质。我们表明，语言模型启发的知识图嵌入方法可以产生改进的知识图嵌入和细粒度实体类型表示。我们的工作还表明，共同建模结构化知识元素和语言可以改善两者。
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.
