Abstract
Typically, Few-shot Continual Relation Extraction (FCRE) models must balance retaining prior knowledge while adapting to new tasks with extremely limited data. However, real-world scenarios may also involve unseen or undetermined relations that existing methods still struggle to handle. To address these challenges, we propose a novel approach that leverages the Open Information Extraction concept of Knowledge Graph Construction (KGC). Our method not only exposes models to all possible pairs of relations, including determined and undetermined labels not available in the training set, but also enriches model knowledge with diverse relation descriptions, thereby enhancing knowledge retention and adaptability in this challenging scenario. In the perspective of KGC, this is the first work explored in the setting of Continual Learning, allowing efficient expansion of the graph as the data evolves. Experimental results demonstrate our superior performance compared to other state-of-the-art FCRE baselines, as well as the efficiency in handling dynamic graph construction in this setting.
Abstract (translated)
通常，少量样本连续关系抽取（FCRE）模型必须在保留先前知识的同时适应仅具有极少量数据的新任务。然而，在现实世界中可能会遇到未见过或不确定的关系类型，现有方法仍然难以处理这种情况。为了解决这些挑战，我们提出了一种新的方法，该方法利用了开放信息抽取中的知识图谱构建（KGC）概念。我们的方法不仅使模型接触到所有可能的实体对关系组合，包括训练集中不可用的已知和未知标签，而且还通过多样化的关系描述来丰富模型的知识库，从而在这一挑战性场景中增强了知识保留能力和适应能力。从KGC的角度来看，这是首次探索连续学习环境下的工作，允许随着数据的发展高效地扩展图谱。实验结果表明，在处理动态图构建方面，与其它最先进的FCRE基准相比，我们的方法表现出色且更有效率。
URL
https://arxiv.org/abs/2502.16648
