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.

翻译：大型制造企业因不同部门维护的数据孤岛而面临信息检索挑战，导致跨数据库的数据不一致与错位。本文介绍了一项针对卫星板设计所用电子元器件资质数据集成与检索的实践经验。由于数据孤岛的存在，设计人员无法即时确定单个元器件的资质状态。然而，在规划阶段——即在生产前发布装配图纸时——这一过程至关重要，可用于优化新资质申请并避免重复工作。为解决此问题，我们提出了一条流水线：利用虚拟知识图谱构建异构数据源的统一视图，并通过大语言模型增强检索能力、减少数据清洗中的人工投入。资质检索通过基于本体的数据访问方法实现结构化查询，同时结合向量搜索机制基于相似文本属性检索资质。通过比较成本效益分析，我们证明该流水线在长期效率方面优于仅依赖大语言模型的方法（如检索增强生成）。