用大模型构建领域知识图谱，助力智能决策与系统规划
Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models
- 基于大模型与电子创新本体，分步构建领域知识图谱
- 在类识别、关系构建上显著优于GraphGPT等基线方法
- 适用于汽车电控等复杂系统规划，支持可解释推理
本文提出一种基于知识图谱的新方法，旨在实现结构化信息的实时获取，支持可操作的技术情报和提升信息物理系统规划能力。框架包含文本挖掘流程：信息检索、关键词提取、语义网络构建及主题地图可视化。随后，基于电子与创新本体的管道，采用选择性知识图谱构建（KGC）方法，支持多目标决策，聚焦信息物理系统。以汽车电气系统为应用领域，验证了该方法的可扩展性。结果表明，在预定义数据集上，该构建过程在类别识别、关系构造和正确'子类'分类方面，性能显著优于GraphGPT、bi-LSTM与Transformer REBEL。此外，还展示了推理应用场景，并与Wikidata对比，凸显其优势。
原文摘要 · Abstract (English)
In this paper we propose a novel approach based on knowledge graphs to provide timely access to structured information, to enable actionable technology intelligence, and improve cyber-physical systems planning. Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization. Following this data exploration process, we employ a selective knowledge graph construction (KGC) approach supported by an electronics and innovation ontology-backed pipeline for multi-objective decision-making with a focus on cyber-physical systems. We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable. Our results demonstrate that our construction process outperforms GraphGPT as well as our bi-LSTM and transformer REBEL with a pre-defined dataset by several times in terms of class recognition, relationship construction and correct "sublass of" categorization. Additionally, we outline reasoning applications and provide a comparison with Wikidata to show the differences and advantages of the approach.
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