Abstract
The growing demand for efficient knowledge graph (KG) enrichment leveraging external corpora has intensified interest in relation extraction (RE), particularly under low-supervision settings. To address the need for adaptable and noise-resilient RE solutions that integrate seamlessly with pre-trained large language models (PLMs), we introduce SCoRE, a modular and cost-effective sentence-level RE system. SCoRE enables easy PLM switching, requires no finetuning, and adapts smoothly to diverse corpora and KGs. By combining supervised contrastive learning with a Bayesian k-Nearest Neighbors (kNN) classifier for multi-label classification, it delivers robust performance despite the noisy annotations of distantly supervised corpora. To improve RE evaluation, we propose two novel metrics: Correlation Structure Distance (CSD), measuring the alignment between learned relational patterns and KG structures, and Precision at R (P@R), assessing utility as a recommender system. We also release Wiki20d, a benchmark dataset replicating real-world RE conditions where only KG-derived annotations are available. Experiments on five benchmarks show that SCoRE matches or surpasses state-of-the-art methods while significantly reducing energy consumption. Further analyses reveal that increasing model complexity, as seen in prior work, degrades performance, highlighting the advantages of SCoRE's minimal design. Combining efficiency, modularity, and scalability, SCoRE stands as an optimal choice for real-world RE applications.
Abstract (translated)
对知识图谱（KG）高效扩充的需求日益增长，尤其是通过利用外部语料库进行关系抽取（RE），这种需求在低监督设置下尤为显著。为了解决适应性强且抗噪能力强的RE解决方案的需求，并使其能够与预训练的大规模语言模型（PLMs）无缝集成，我们提出了SCoRE系统——这是一个模块化且成本效益高的句子级RE系统。SCoRE支持轻松更换PLM，无需微调即可平滑地适应各种语料库和KG。 通过将监督对比学习与贝叶斯k-Nearest Neighbors（kNN）分类器结合用于多标签分类，它在远距离监督语料库的噪声注释下仍能提供稳健性能。为了改进RE评估，我们提出了两个新的指标：Correlation Structure Distance (CSD)，用以衡量所学的关系模式与KG结构之间的对齐程度；以及Precision at R (P@R)，用于评估推荐系统的效用。 此外，我们发布了Wiki20d，这是一个基准数据集，在此数据集中仅使用从KG衍生出的注释来模拟现实世界的RE条件。在五个基准上的实验显示，SCoRE与现有方法相比性能相匹配甚至超越，并且显著减少了能耗。进一步分析发现，增加模型复杂度（如先前研究中所见）会降低性能，这突显了SCoRE最小化设计的优势。 结合效率、模块性和可扩展性，SCoRE是现实世界RE应用的理想选择。
URL
https://arxiv.org/abs/2507.06895
