UMBC ebiquity
EMNLP Workshop on Deep Learning Inside Out
On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling
November 19, 2020
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.
