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

2026-03-20

AI Summary

This study addresses the challenge of fragmented qualification data for electronic components in aerospace engineering, which is scattered across multiple heterogeneous systems and impedes efficient decision-making during design phases. To overcome this, the authors propose a semantic integration approach that synergistically combines virtual knowledge graphs with large language models. By leveraging an ontology-based data access (OBDA) framework alongside vector retrieval mechanisms, the method enables unified and efficient querying of qualification information across disparate data silos. The approach maintains strong semantic consistency while substantially reducing manual data curation costs. Compared to conventional retrieval-augmented generation (RAG) or pure large-model solutions, it demonstrates superior performance in retrieval accuracy, computational efficiency, and long-term operational cost, thereby effectively minimizing redundant certification efforts.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• data silos
• electronic component qualifications
• information retrieval
• aerospace domain
• qualification status

Innovation

• Virtual Knowledge Graphs
• Large Language Models
• Ontology-based Data Access
• Semantic Data Integration
• Vector Search