Papers with OpenRE
Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data (D19-1)
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| Challenge: | Existing methods to extract relational facts from open domain corpora are time-consuming and human-intensive. |
| Approach: | They propose a framework to learn similarity metrics of relations from labeled data . they propose to transfer relational knowledge to identify novel relations in unlabeled data. |
| Outcome: | Experiments on two real-world datasets show that the proposed framework improves compared with state-of-the-art methods. |
Actively Supervised Clustering for Open Relation Extraction (2023.acl-long)
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| Challenge: | Existing methods for Open Relation Extraction (OpenRE) use a two-stage pipeline, which learns relation representations and assignments in the first stage, then manually labels relation for each cluster. |
| Approach: | They propose a method that performs relation learning and relation labeling simultaneously without a significant increase in human effort. |
| Outcome: | The proposed method improves existing SOTA methods by 13.8% and 10.6% on two datasets. |
UOREX: Towards Uncertainty-Aware Open Relation Extraction (2025.naacl-long)
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| Challenge: | Existing methods for relation extraction are limited by their inability to accurately self-assess their performance. |
| Approach: | They propose an approach that effectively models a part of the epistemic uncertainty within OpenRE by preventing overconfident errors. |
| Outcome: | The proposed approach improves OpenRE reliability by preventing overconfident errors. |
Towards a More Generalized Approach in Open Relation Extraction (2025.acl-long)
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| Challenge: | Existing OpenRE methods assume unlabeled data is a mixture of known and novel instances. |
| Approach: | They propose a generalized OpenRE setting that considers unlabeled data as a mixture of known and novel instances. |
| Outcome: | The proposed framework outperforms baselines in relation classification and clustering on three benchmark datasets. |
Element Intervention for Open Relation Extraction (2021.acl-long)
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| Challenge: | Current OpenRE models are often trained on the datasets generated from distant supervision, which often results in instability and makes the model easily collapsed. |
| Approach: | They propose to use a causal model to identify relation instances referring to the same relation . they propose to perform Element Interventions on context and entities respectively . |
| Outcome: | The proposed method outperforms existing methods and is robust across datasets. |
Open Hierarchical Relation Extraction (2021.naacl-main)
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| Challenge: | Existing OpenRE methods cast different relation types in isolation without considering their hierarchical dependency. |
| Approach: | They propose a framework to establish bidirectional connections between OpenRE and relation hierarchies by integrating hierarchy information into relation representations. |
| Outcome: | The proposed framework outperforms state-of-the-art models on relation clustering and hierarchy expansion. |
LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models (2025.emnlp-main)
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| Challenge: | Existing studies focus on building models that can only handle predefined relations . however, their reliance on human annotation limits their practicality . |
| Approach: | They propose an open relation extraction framework that can generalize to new relations not encountered during training. |
| Outcome: | The proposed framework can generalize to new relations not encountered during training. |
MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering (2022.emnlp-main)
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| Challenge: | Existing methods for open relation extraction (OpenRE) focus on labeled and pre-defined instances, which are costly to acquire in reality. |
| Approach: | They propose a framework that can extract relations without pre-defined types from open-domain corpus with efficient knowledge transfer from a few pre-determined relational instances. |
| Outcome: | The proposed framework achieves the new SOTA results for OpenRE on different datasets. |
When Phrases Meet Probabilities: Enabling Open Relation Extraction with Cooperating Large Language Models (2024.acl-long)
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| Challenge: | Existing clustering-based open relation extraction methods use pre-trained language models . embeddings from language models are high-dimensional and anisotropic, so there is a gap . |
| Approach: | They propose a framework that makes two LLMs work collaboratively to achieve clustering. |
| Outcome: | The proposed framework outperforms existing methods by 1.4%3.13% on different datasets. |
A Relation-Oriented Clustering Method for Open Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for open relation extraction (OpenRE) are designed for predefined relations, which cannot deal with new emerging relations in the real world. |
| Approach: | They propose a relation-oriented clustering model that leverages readily available labeled data to learn a relationship-oriented representation. |
| Outcome: | The proposed model reduces error rate by 29.2% and 15.7% on two datasets compared with current SOTA methods. |
Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting (2023.emnlp-main)
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| Challenge: | Existing approaches to open-world relation extraction assume that all instances of unlabeled data belong to novel classes. |
| Approach: | They propose a method that classifies relations from known and novel classes within unlabeled data. |
| Outcome: | The proposed method outperforms existing methods on Open-world RE benchmarks. |