  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.
