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Source note2 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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Source

Plain-Language Brief

The paper proposes a new method called Reasoning-guided progressive Open Relation Extraction (ReaORE) to improve the performance of Open Relation Extraction (OpenRE). This method addresses the limitations of current OpenRE approaches by using coarse-to-fine relation reasoning, filtering and predicting relations in two stages. The experiments show that ReaORE outperforms existing baselines on two widely used OpenRE datasets.

What the Document Says

  • The paper presents a new method called ReaORE for Open Relation Extraction (OpenRE).
  • ReaORE addresses the challenges in current OpenRE approaches by using coarse-to-fine relation reasoning.
  • It consists of two stages: relation filtering and relation prediction.

Key Concepts Mentioned

  • Open Relation Extraction
  • Reasoning-guided progressive OpenRE (ReaORE)
  • Large Language Models (LLMs)
  • Relation filtering
  • Relation prediction

Claims Made by the Document

  • Current OpenRE approaches have limitations.
  • ReaORE outperforms existing baselines on two widely used OpenRE datasets.

Evidence Used

  • It is stated that 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.
  • The paper demonstrates the performance of ReaORE through extensive experiments on two widely used OpenRE datasets.

Methods / Approach

  • Reasoning-guided progressive approach
  • Relation filtering
  • Relation prediction

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

It is not specified if there are limitations to the method proposed by the authors. However, as with any research, there may be potential limitations that could be uncovered through further analysis or testing.

Open Questions

  • What are the specific limitations of current OpenRE approaches that ReaORE addresses?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not provide detailed explanations about the specific limitations of current OpenRE approaches. However, it mentions that they suffer from poor generalization or lack sufficient discriminative capacity.

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.