Open Relation Modeling: Learning to Define Relations between Entities

Jie Huang, Kevin Chang, Jinjun Xiong, Wen-mei Hwu


Abstract
Relations between entities can be represented by different instances, e.g., a sentence containing both entities or a fact in a Knowledge Graph (KG). However, these instances may not well capture the general relations between entities, may be difficult to understand by humans, even may not be found due to the incompleteness of the knowledge source. In this paper, we introduce the Open Relation Modeling problem - given two entities, generate a coherent sentence describing the relation between them. To solve this problem, we propose to teach machines to generate definition-like relation descriptions by letting them learn from defining entities. Specifically, we fine-tune Pre-trained Language Models (PLMs) to produce definitions conditioned on extracted entity pairs. To help PLMs reason between entities and provide additional relational knowledge to PLMs for open relation modeling, we incorporate reasoning paths in KGs and include a reasoning path selection mechanism. Experimental results show that our model can generate concise but informative relation descriptions that capture the representative characteristics of entities.
Anthology ID:
2022.findings-acl.26
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
297–308
Language:
URL:
https://aclanthology.org/2022.findings-acl.26
DOI:
10.18653/v1/2022.findings-acl.26
Bibkey:
Cite (ACL):
Jie Huang, Kevin Chang, Jinjun Xiong, and Wen-mei Hwu. 2022. Open Relation Modeling: Learning to Define Relations between Entities. In Findings of the Association for Computational Linguistics: ACL 2022, pages 297–308, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Open Relation Modeling: Learning to Define Relations between Entities (Huang et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.26.pdf
Software:
 2022.findings-acl.26.software.zip
Video:
 https://aclanthology.org/2022.findings-acl.26.mp4
Code
 jeffhj/open-relation-modeling
Data
Open Relation Modeling