PUBLICATION
A Web Engineering Method for AI-Assisted Knowledge Graph Construction in Industrial Domains
Type
Conference Paper
Year
2026
Authors
Maheshika Hansamalee Walpola M.Sc.
Prof. Dr.-Ing. Martin Gaedke
Research Area
Event
26th International Conference on Web Engineering
Published in
26th International Conference on Web Engineering, Lyon, France
ISBN/ISSN
tba
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Abstract
Predictive maintenance systems rely on machine learning (ML) to anticipate equipment failures, but domain engineers often cannot explain why a prediction was made, limiting trust and adoption. Knowledge graphs (KGs) can connect predictions with structured domain knowledge, but building industrial KGs requires semantic web expertise domain engineers lack. This proposal addresses three problems: ML predictions lack human-readable explanations, KG construction has no reproducible method with formal validation, and no web-based approach enables non-technical users to build and query KGs. The proposed Web Engineering method integrates large language model (LLM) assistance across the KG lifecycle, from ontology elicitation and data validation to natural language querying and explanation generation. The method is realised through a web portal following End-User Development (EUD) principles, enabling domain engineers to construct ontologies, ingest data, query the KG, and inspect prediction explanations. Evaluation uses CMP semiconductor manufacturing, with a second use case planned in wind turbine monitoring.
Reference
TBA
