基于开放信息抽取的少样本持续关系抽取
Few-shot Continual Relation Extraction via Open Information Extraction
AI总结：
提出利用开放信息抽取与知识图谱构建的少样本持续关系抽取方法，处理未见关系，增强知识保留与适应性，实验优于现有基线。
摘要：
通常，少样本持续关系抽取（FCRE）模型必须在保留先前知识的同时，适应数据极其有限的新任务。然而，现实场景中可能涉及未见或未确定的关系，现有方法仍难以处理。为解决这些挑战，我们提出了一种新颖的方法，利用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暴露于所有可能的关系对，包括训练集中未提供的确定和未确定标签，还通过多样化的关系描述丰富了模型知识，从而增强了在这种挑战性场景下的知识保留和适应性。从KGC的角度看，这是首次在持续学习设置中探索的工作，允许随着数据演变高效扩展图谱。实验结果表明，与其他最先进的FCRE基线相比，我们具有优越的性能，并且在此设置中处理动态图谱构建的效率也很高。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
