发表机构
Infineon Technologies AG（英飞凌科技股份公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对半导体供应链风险情报提取与排序的需求，提出端到端流程，结合LLMs与三层排序机制处理五家企业文档，生成高有效率的风险机会矩阵，识别贸易限制为主要风险。
AI 中文摘要
半导体供应链面临地缘政治紧张、地理集中以及快速技术变革带来的日益加剧的风险，但目前尚无可扩展的系统能够持续从公开的企业披露信息中提取、结构化并对风险情报进行优先级排序。我们提出了一种端到端流程，用于检索半导体企业的文档，并利用大语言模型（LLMs）提取其中描述的风险与机会。该流程将这些内容组织成知识图谱，将每个条目与其类别、来源及相关事件关联，随后合并重复项并通过三层机制进行排序，该机制结合了算法公式、LLM相关性调整以及专家验证。将该流程应用于价值链上的五家企业后，共生成76207个带分值的条目，经独立检查发现其中92.6%为有效条目；自动排序与专家判断的平均斯皮尔曼相关系数，风险为0.55，机会为0.72，生成的矩阵识别出贸易限制是所有企业共有的主要风险。
英文摘要
Semiconductor supply chains face escalating risks from geopolitical tensions, geographic concentration, and rapid technological shifts, yet no scalable system continuously extracts, structures, and prioritizes risk intelligence from public corporate disclosures. We present an end-to-end pipeline that retrieves corporate documents for semiconductor companies and uses large language models (LLMs) to extract the risks and opportunities they describe. It organizes these into a knowledge graph linking each item to its category, sources, and related events, then merges duplicates and ranks them with a three-layer mechanism combining an algorithmic formula, an LLM relevance adjustment, and expert validation. Applied to five companies across the value chain, the pipeline produces 76,207 scored items, of which an independent check finds 92.6% valid. The automated rankings match expert judgment at an average Spearman correlation of 0.55 for risks and 0.72 for opportunities, and the resulting matrices identify trade restrictions as the dominant cross-company risk.
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