# Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction

arXiv: https://arxiv.org/abs/2401.11800 · HTML: https://mlsanity.com/paper/2401.11800/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2024.

## Built on by (most cited first, 6 of 6)

- [Automated Construction of Theme-specific Knowledge Graphs](https://mlsanity.com/paper/2404.19146/) (2024)
- [Knowledge-Driven Cross-Document Relation Extraction](https://mlsanity.com/paper/2405.13546/) (2024)
- [Neuro-Symbolic Artificial Intelligence: A Task-Directed Survey in the Black-Box Models Era](https://mlsanity.com/paper/2603.03177/) (2026)
- [Analyzing the Influence of Knowledge Graph Information on Relation Extraction](https://mlsanity.com/paper/2506.16343/) (2025)
- [Multi-Relation Extraction in Entity Pairs using Global Context](https://mlsanity.com/paper/2507.22926/) (2025)
- [DOREMI: Optimizing Long Tail Predictions in Document-Level Relation Extraction](https://mlsanity.com/paper/2601.11190/) (2026)

## Abstract (arXiv)

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.