Ontology-Driven Structural Regularization for Document-Level Relation Extraction

Consistency-based repair is widely assumed to improve LLM-based document-level relation extraction (DocRE), but this assumption can fail when the benchmark itself is incompletely annotated. Whether such repair helps depends strongly on how closed the gold annotations are under the constraints it enforces. We introduce $Δ_{\rm closure}$, the rate at which gold annotations lack facts implied by the very rules used for repair, computable from the annotations and rules alone before any repair is run. The same typed constraints drive CONSISTRE, which applies them at inference time (constraint feedback for reflection, deterministic closure) and at training time (distillation and consistency-aware reinforcement learning). On 998 DocRED documents, whose Re-DocRED revision lowers $Δ_{\rm closure}$ from 1.71% to 0.257%, rescoring identical predictions reverses the method ranking: Self-Refine leads under DocRED, while a constraint-feedback variant moves from fifth place to first. On the near-closed DWIE, constraint feedback with chain-of-thought beats three generic reflection baselines by 0.046-0.107 micro-F1 ($p=0.0002$), and deterministic closure of the base output scores higher still at $3.6\times$ lower cost. A reinforcement-learning gain on DocRED (+0.025) does not survive Re-DocRED rescoring. Consistency repair pays off where annotations are closed, and $Δ_{\rm closure}$ tells in advance whether they are.

Open Relation Extraction (OpenRE) requires a model to extract unseen relations between head and tail entities from unstructured text for real-world applications. The core challenge of OpenRE lies in achieving reliable generalization to unseen relation types. Current OpenRE approaches either employ clustering techniques, which cannot generate relation labels and suffer from poor generalization, or rely on direct relation label generation via Large Language Models (LLMs), which lack sufficient discriminative capacity to distinguish easily confused relations. To address these limitations, we propose Reasoning-guided progressive OpenRE (ReaORE), a framework for performing relation extraction through coarse-to-fine relation reasoning. Specifically, ReaORE consists of two key stages: (i) relation filtering, which reasons over multiple aspects to understand relations and instances, yielding an initial relation set, and further supplements and filters relations via embedding-based similarity to ensure the target relation is included; (ii) relation prediction, which aims to predict the target relations from the above set via fine-grained comparative reasoning to better distinguish easily confused relations. Extensive experiments on two widely used OpenRE datasets demonstrate that ReaORE outperforms existing baselines.

Cross-document relation extraction (RE) aims to identify relations between the head and tail entities located in different documents. Existing approaches typically adopt the paradigm of ``Small Language Model (SLM) + Classifier''. However, the limited language understanding ability of SLMs hinders further improvement of their performance. In this paper, we conduct a preliminary study to explore the performance of Large Language Models (LLMs) in cross-document RE. Despite their extensive parameters, our findings indicate that LLMs do not consistently surpass existing SLMs. Further analysis suggests that the underperformance is largely attributed to the challenges posed by the numerous predefined relations. To overcome this issue, we propose an LLM-based Hierarchical Classification model for cross-document RE (HCRE), which consists of two core components: 1) an LLM for relation prediction and 2) a hierarchical relation tree derived from the predefined relation set. This tree enables the LLM to perform hierarchical classification, where the target relation is inferred level by level. Since the number of child nodes is much smaller than the size of the entire predefined relation set, the hierarchical relation tree significantly reduces the number of relation options that LLM needs to consider during inference. However, hierarchical classification introduces the risk of error propagation across levels. To mitigate this, we propose a prediction-then-verification inference strategy that improves prediction reliability through multi-view verification at each level. Extensive experiments show that HCRE outperforms existing baselines, validating its effectiveness.