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

TL;DR

This paper introduces a pipeline combining Virtual Knowledge Graphs, LLMs, and ontology-based data access to improve electronic component qualification data retrieval in aerospace, reducing manual effort and enhancing efficiency.

Contribution

It presents a novel integrated approach using knowledge graphs, LLMs, and vector search for semantic data integration in aerospace component qualification.

Findings

Outperforms RAG in cost-benefit analysis

Reduces manual data cleansing efforts

Enhances retrieval accuracy and efficiency

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.