Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE) tasks. While most previous RE methods focus on leveraging deterministic KGs, uncertain KGs, which assign a confidence score for each relation instance, can provide prior probability distributions of relational facts as valuable external knowledge for RE models. This paper proposes to exploit uncertain knowledge to improve relation extraction. Specifically, we introduce ProBase, an uncertain KG that indicates to what extent a target entity belongs to a concept, into our RE architecture. We then design a novel multi-view inference framework to systematically integrate local context and global knowledge across three views: mention-, entity- and concept-view. The experimental results show that our model achieves competitive performances on both sentence- and document-level relation extraction, which verifies the effectiveness of introducing uncertain knowledge and the multi-view inference framework that we design.

翻译：知识图表(KGs)被广泛用于促进关系提取任务。虽然大多数以前的RE方法都侧重于利用确定性KGs,但不确定的KGs(给每个关系实例定了一个信任分)可以提供关系事实的概率分布,作为RE模型的宝贵外部知识。本文提议利用不确定的知识来改进关系提取。具体地说,我们将一个不确定的KG(ProBase)引入我们的RE结构,它表明目标实体在多大程度上属于一个概念。然后我们设计一个新的多视角推理框架,系统地将地方背景和全球知识纳入三个观点:提及、实体和概念观点。实验结果显示,我们的模型在句级和文件级关系提取两方面都取得了竞争性业绩,从而验证了引入不确定知识和我们设计的多视角推理框架的有效性。