Abstract
Despite becoming a prevailing paradigm for organizing knowledge, most knowledge graphs (KGs) suffer from the low-resource issue due to the deficiency of data sources. The enrichment of KGs by automatic knowledge graph completion is impeded by the intrinsic long-tail property of KGs. In spite of their prosperity, existing few-shot learning-based models have difficulty alleviating the impact of the long-tail issue on low-resource KGs because of the lack of training tasks. To tackle the challenging long-tail issue on low-resource KG completion, in this paper, we propose a novel few-shot low-resource knowledge graph completion framework, which is composed of three components, i.e., few-shot learner, task generator, and task selector. The key idea is to generate and then select the beneficial few-shot tasks that complement the current tasks and enable the optimization of the few-shot learner using the selected few-shot tasks. Extensive experiments conducted on several real-world knowledge graphs validate the effectiveness of our proposed method.- Anthology ID:
- 2023.findings-acl.455
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7252–7264
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.455
- DOI:
- 10.18653/v1/2023.findings-acl.455
- Cite (ACL):
- Shichao Pei, Qiannan Zhang, and Xiangliang Zhang. 2023. Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7252–7264, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation (Pei et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.findings-acl.455.pdf