Abstract
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.- Anthology ID:
- 2020.deelio-1.11
- Volume:
- Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- DeeLIO
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 89–99
- Language:
- URL:
- https://aclanthology.org/2020.deelio-1.11
- DOI:
- 10.18653/v1/2020.deelio-1.11
- Cite (ACL):
- Rajat Patel and Francis Ferraro. 2020. On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling. In Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 89–99, Online. Association for Computational Linguistics.
- Cite (Informal):
- On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling (Patel & Ferraro, DeeLIO 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.deelio-1.11.pdf
- Code
- rajathpatel23/joint-kge-fnet-lm
- Data
- FIGER