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    <abstract>We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as “Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.” To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation.</abstract>
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%0 Conference Proceedings
%T DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
%A Huang, Jie
%A Zhu, Kerui
%A Chang, Kevin Chen-Chuan
%A Xiong, Jinjun
%A Hwu, Wen-mei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F huang-etal-2022-deer
%X We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as “Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.” To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation.
%R 10.18653/v1/2022.emnlp-main.448
%U https://aclanthology.org/2022.emnlp-main.448
%U https://doi.org/10.18653/v1/2022.emnlp-main.448
%P 6686-6698

Markdown (Informal)

[DEER: Descriptive Knowledge Graph for Explaining Entity Relationships](https://aclanthology.org/2022.emnlp-main.448) (Huang et al., EMNLP 2022)

• DEER: Descriptive Knowledge Graph for Explaining Entity Relationships (Huang et al., EMNLP 2022)

ACL

• Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, Jinjun Xiong, and Wen-mei Hwu. 2022. DEER: Descriptive Knowledge Graph for Explaining Entity Relationships. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6686–6698, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.