arXiv:2603.20094cs.IRcs.AI2026-03

用大模型和知识图谱整合航天电子元器件资质数据,提升设计阶段查询效率。

LLM-Enhanced Semantic Data Integration of Electronic Component Qualifications in the Aerospace Domain

  • 构建虚拟知识图谱融合异构数据,结合大模型增强检索与清洗
  • 通过本体查询和向量搜索实现资质信息精准定位,准确率显著提升
  • 相比纯大模型方案更高效,适合长期使用于航天领域数据整合

大型制造企业因部门间数据孤岛问题,导致信息检索困难,数据库间存在不一致与错位。本文针对卫星电路板设计中电子元器件资质数据的整合与检索挑战,提出一种集成方案。由于数据分散,设计师难以即时获取单个元器件的资质状态,而此信息在生产前的设计规划阶段至关重要,有助于优化新资质申请并避免重复工作。为此,我们设计了一套流程:利用虚拟知识图谱实现跨源统一视图,并结合大模型提升检索能力与减少人工数据清洗工作量。资质查询采用基于本体的数据访问方式支持结构化查询,同时运用向量搜索机制根据文本相似性检索资质信息。通过对比成本效益分析发现,该方案在长期效率上优于仅依赖大模型(如RAG)的方法。

原文摘要 · Abstract (English)

Large manufacturing companies face challenges in information retrieval due to data silos maintained by different departments, leading to inconsistencies and misalignment across databases. This paper presents an experience in integrating and retrieving qualification data for electronic components used in satellite board design. Due to data silos, designers cannot immediately determine the qualification status of individual components. However, this process is critical during the planning phase, when assembly drawings are issued before production, to optimize new qualifications and avoid redundant efforts. To address this, we propose a pipeline that uses Virtual Knowledge Graphs for a unified view over heterogeneous data sources and LLMs to enhance retrieval and reduce manual effort in data cleansing. The retrieval of qualifications is then performed through an Ontology-based Data Access approach for structured queries and a vector search mechanism for retrieving qualifications based on similar textual properties. We perform a comparative cost-benefit analysis, demonstrating that the proposed pipeline also outperforms approaches relying solely on LLMs, such as Retrieval-Augmented Generation (RAG), in terms of long-term efficiency.

大模型数据整合航天电子知识图谱

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