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    <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.</abstract>
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%0 Conference Proceedings
%T On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling
%A Patel, Rajat
%A Ferraro, Francis
%S Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F patel-ferraro-2020-complementary
%X 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.
%R 10.18653/v1/2020.deelio-1.11
%U https://aclanthology.org/2020.deelio-1.11
%U https://doi.org/10.18653/v1/2020.deelio-1.11
%P 89-99

Markdown (Informal)

[On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling](https://aclanthology.org/2020.deelio-1.11) (Patel & Ferraro, DeeLIO 2020)

• On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling (Patel & Ferraro, DeeLIO 2020)

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.