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Source note3 min read

ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models

Source

arXiv

arxiv.org/abs/2606.26986v1

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Plain-Language Brief

The paper introduces a new method called Reasoning-guided progressive Open Relation Extraction (ReaORE) to improve the extraction of unseen relations between entities in text for real-world applications. Current methods either group entities without generating relation labels or use large language models that can't distinguish similar but different relations well. ReaORE addresses this by using a two-stage process: first, it reasons over multiple aspects to understand relations and instances, then filters and supplements them; second, it predicts the target relations through fine-grained comparative reasoning. The results of experiments on two popular datasets show that ReaORE performs better than existing methods.

What the Document Says

  • The paper presents a new method called Reasoning-guided progressive Open Relation Extraction (ReaORE).
  • Current methods for Open Relation Extraction either use clustering techniques or direct relation label generation via Large Language Models.
  • These current methods have limitations: clustering can't generate relation labels and suffers from poor generalization, while LLMs lack discriminative capacity.
  • ReaORE addresses these limitations by using a two-stage process: relation filtering and relation prediction.
  • In the relation filtering stage, ReaORE reasons over multiple aspects to understand relations and instances, filters and supplements them based on embedding-based similarity.
  • In the relation prediction stage, it predicts the target relations through fine-grained comparative reasoning to distinguish easily confused relations.
  • Extensive experiments on two OpenRE datasets show that ReaORE outperforms existing baselines.

Key Concepts Mentioned

  • Open Relation Extraction
  • ReaORE
  • Large Language Models
  • relation filtering
  • relation prediction
  • comparative reasoning

Claims Made by the Document

  • Current OpenRE approaches have limitations.
  • ReaORE addresses these limitations with a two-stage process.
  • ReaORE outperforms existing baselines.

Evidence Used

  • The core challenge of OpenRE lies in achieving reliable generalization to unseen relation types.
  • To address these limitations, we propose Reasoning-guided progressive OpenRE (ReaORE)
  • Extensive experiments on two widely used OpenRE datasets demonstrate that ReaORE outperforms existing baselines.

Methods / Approach

  • Reasoning-guided progressive Open Relation Extraction (ReaORE)

Limitations Stated by the Authors

  • Clustering techniques cannot generate relation labels and suffer from poor generalization.
  • Large Language Models lack sufficient discriminative capacity to distinguish easily confused relations.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The paper does not discuss potential limitations of the ReaORE method itself.

Open Questions

  • What are the specific algorithms used in the relation filtering and prediction stages?
  • How does the paper ensure the generalization of ReaORE to a wide range of text types?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper focuses on the methodology and results of ReaORE, but it doesn't provide detailed information about the algorithms used within the method.

Raw Abstract / Excerpt

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.