用领域微调模型提升走私网络知识图谱构建效率与质量
FineREX: Fine-Tuned NER-RE for Human Smuggling Knowledge Graphs
- 基于微调大模型实现法律文本中的实体与关系抽取
- 实体和关系准确率分别提升15.50%和31.46%
- 处理速度加快50%，噪声减少近半，适合司法情报分析
法庭文件包含关于人口走私网络的重要证据，但这些信息常隐藏在非结构化、术语密集的法律文档中。尽管大语言模型（LLMs）可通过自动化信息抽取支持知识图谱构建，现有方法多依赖通用模型，无法适配该领域的实体与关系定义。我们提出FineREX，一个围绕微调大模型构建的命名实体识别与关系抽取（NER-RE）的轻量级知识图谱构建流程。基于512个文本片段的手动标注数据集，FineREX相比更大规模通用基线，在实体和关系的F1得分上分别提升15.50%和31.46%。结果生成的知识图谱显著降低法律噪声近一半，长文档中的节点重复率从17.78%降至11.17%。通过省去文档重写和冗余抽取环节，端到端处理时间减少50.0%。实验表明，领域微调可显著超越更大通用模型，同时提升非法网络分析中知识图谱的质量与效率。
原文摘要 · Abstract (English)
Court proceedings contain valuable evidence about human smuggling networks, but this information is often buried within unstructured, jargon-heavy legal documents. While large language models (LLMs) can support knowledge graph construction through automated information extraction, existing approaches rely on general-purpose models that are not tailored to the entity and relationship definitions required in this domain. We introduce FineREX, a streamlined knowledge graph construction pipeline built around a fine-tuned LLM for named entity recognition and relationship extraction (NER-RE). Using a manually annotated dataset of $512$ text chunks, FineREX achieves absolute improvements of 15.50% and 31.46% in entity and relationship F1-score, respectively, compared to a larger general-purpose baseline. These gains translate into higher-quality knowledge graphs, reducing legal noise by nearly half and lowering node duplication on long documents from 17.78% to 11.17%. By eliminating document rewriting and redundant extraction stages, FineREX also reduces end-to-end processing time by 50.0%. Our results demonstrate that domain-specific fine-tuning can substantially outperform larger general-purpose models while improving both the quality and efficiency of knowledge graph construction for illicit network analysis.
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