Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document as opposed to the traditional RE setting where a single sentence is input. Existing approaches rely on logical reasoning or contextual cues from entities. This paper reframes document-level RE as link prediction over a knowledge graph with distinct benefits: 1) Our approach combines entity context with document-derived logical reasoning, enhancing link prediction quality. 2) Predicted links between entities offer interpretability, elucidating employed reasoning. We evaluate our approach on three benchmark datasets: DocRED, ReDocRED, and DWIE. The results indicate that our proposed method outperforms the state-of-the-art models and suggests that incorporating context-based link prediction techniques can enhance the performance of document-level relation extraction models.

翻译：文档级关系抽取（DocRE）面临在文档中识别实体间关系的挑战，这与传统关系抽取设置（以单个句子为输入）不同。现有方法依赖逻辑推理或实体上下文线索。本文重新将文档级关系抽取定义为知识图谱上的链接预测，具有显著优势：1）我们的方法将实体上下文与文档驱动的逻辑推理相结合，提升了链接预测质量；2）实体间的预测链接提供了可解释性，阐明了所采用的推理过程。我们在三个基准数据集（DocRED、ReDocRED和DWIE）上评估了该方法。结果表明，我们提出的方法优于当前最先进模型，并表明融入基于上下文的链接预测技术可提升文档级关系抽取模型的性能。