# A Relation-Oriented Clustering Method for Open Relation Extraction

arXiv: https://arxiv.org/abs/2109.07205 · HTML: https://mlsanity.com/paper/2109.07205/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Assessing BERT's Syntactic Abilities](https://mlsanity.com/paper/1901.05287/) (2019)
- [Dissecting Contextual Word Embeddings: Architecture and Representation](https://mlsanity.com/paper/1808.08949/) (2018)
- [SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction](https://mlsanity.com/paper/2004.02438/) (2020)
- [Unsupervised Open Relation Extraction](https://mlsanity.com/paper/1801.07174/) (2018)
- [Revisiting Unsupervised Relation Extraction](https://mlsanity.com/paper/2005.00087/) (2020)

## Built on by (most cited first, 15 of 15)

- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning](https://mlsanity.com/paper/2210.07733/) (2022)
- [RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2306.04954/) (2023)
- [Open Relation and Event Type Discovery with Type Abstraction](https://mlsanity.com/paper/2212.00178/) (2022)
- [Mere Contrastive Learning for Cross-Domain Sentiment Analysis](https://mlsanity.com/paper/2208.08678/) (2022)
- [A Survey on Open Information Extraction from Rule-based Model to Large Language Model](https://mlsanity.com/paper/2208.08690/) (2022)
- [Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach](https://mlsanity.com/paper/2305.18350/) (2023)
- [PromptORE -- A Novel Approach Towards Fully Unsupervised Relation Extraction](https://mlsanity.com/paper/2304.01209/) (2023)
- [Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery](https://mlsanity.com/paper/2401.06327/) (2024)
- [Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting](https://mlsanity.com/paper/2305.13533/) (2023)
- [Open Set Relation Extraction via Unknown-Aware Training](https://mlsanity.com/paper/2306.04950/) (2023)
- [Actively Supervised Clustering for Open Relation Extraction](https://mlsanity.com/paper/2306.04968/) (2023)
- [Siamese Representation Learning for Unsupervised Relation Extraction](https://mlsanity.com/paper/2310.00552/) (2023)
- [Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2406.11429/) (2024)
- [LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models](https://mlsanity.com/paper/2509.15089/) (2025)

## Abstract (arXiv)

The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE). However, high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clusters cannot explicitly align with the relational semantic classes. In this work, we propose a relation-oriented clustering model and use it to identify the novel relations in the unlabeled data. Specifically, to enable the model to learn to cluster relational data, our method leverages the readily available labeled data of pre-defined relations to learn a relation-oriented representation. We minimize distance between the instance with same relation by gathering the instances towards their corresponding relation centroids to form a cluster structure, so that the learned representation is cluster-friendly. To reduce the clustering bias on predefined classes, we optimize the model by minimizing a joint objective on both labeled and unlabeled data. Experimental results show that our method reduces the error rate by 29.2% and 15.7%, on two datasets respectively, compared with current SOTA methods.