Abstract
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.
Abstract (translated)
大型制造公司因各部门维护的数据孤岛而在信息检索方面面临挑战，这导致跨数据库的数据不一致与错位。本文介绍了在整合与检索卫星板设计所用电子元件资质数据方面的一次实践。由于数据孤岛，设计人员无法立即确定单个元件的资质状态。然而，在规划阶段——即生产前下发装配图纸时——这一流程至关重要，旨在优化新资质认证并避免重复工作。为此，我们提出一种利用虚拟知识图谱对异构数据源提供统一视图、并借助大语言模型增强检索与减少数据清洗人工成本的流程。资质检索随后通过基于本体的数据访问方法实现结构化查询，并通过向量搜索机制基于相似的文本属性检索资质。我们进行了对比成本效益分析，证明所提出的流程在长期效率方面也优于仅依赖大语言模型的方法（如检索增强生成）。
URL
https://arxiv.org/abs/2603.20094
