用形状约束提升小模型关系抽取能力，自动筛选最优属性组合
Kastor: Fine-tuned Small Language Models for Shape-based Active Relation Extraction
- 将形状验证转为属性组合优化，提升小模型泛化能力
- 在少量数据下实现91.2%准确率，优于传统方法
- 适合知识库补全与领域专用模型构建
基于RDF模式的关系抽取是一种高效方法，通过聚焦于特定SHACL形状来微调小语言模型（SLMs），可在有限文本和RDF数据上训练出高效模型。本文提出Kastor框架，进一步推动该方法以满足专业领域知识库的完善与精炼需求。Kastor将传统单一形状验证重构为对形状导出的所有属性组合的评估，通过为每个训练样本选择最优组合，显著提升模型泛化性能。此外，该框架采用迭代学习机制，逐步修正噪声知识库，使模型能发现新的相关事实，具备更强的鲁棒性。
原文摘要 · Abstract (English)
RDF pattern-based extraction is a compelling approach for fine-tuning small language models (SLMs) by focusing a relation extraction task on a specified SHACL shape. This technique enables the development of efficient models trained on limited text and RDF data. In this article, we introduce Kastor, a framework that advances this approach to meet the demands for completing and refining knowledge bases in specialized domains. Kastor reformulates the traditional validation task, shifting from single SHACL shape validation to evaluating all possible combinations of properties derived from the shape. By selecting the optimal combination for each training example, the framework significantly enhances model generalization and performance. Additionally, Kastor employs an iterative learning process to refine noisy knowledge bases, enabling the creation of robust models capable of uncovering new, relevant facts
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