Work

Leveraging Large Language Models and Object Detection for Automated Knowledge Graph Generation from Industrial Schematics

Year: 2026

Type: article

Abstract: Industrial digitalization increasingly requires automated tools capable of extracting structured knowledge from complex engineering documentation, such as Piping and Instrumentation Diagrams (P&IDs). This work proposes an integrated framework that combines object detection and Large Language Models (LLMs) for automated Knowledge Graph (KG) generation. The approach enables the transformation of unstructured P&ID schematics into machine-interpretable representations, supporting data-driven analysis and decision-making. A modular pipeline is developed, including image pre-processing, symbol detection via a YOLO-based model, and identification of semantic relations between schematic elements using LLMs. The proposal also includes the definition of a reference ontology, which is exploited for the construction of the KG, and a diagram dataset designed to test the performance of the object detection model. The KG generation procedure achieves strong results in terms of image reconstruction across a wide set of industrial schematics, while also preserving the semantic integrity and completeness of the original diagrams. The proposed method represents a significant step toward the digitalization of industrial knowledge, bridging traditional engineering documentation and semantic-based technologies.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc10070210