Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation
Shichao Pei, Qiannan Zhang, Xiangliang Zhang · 2023
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 longtail 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 fewshot 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.