Recent advances in foundation models have demonstrated impressive capabilities, but they still face critical limitations in reasoning, explainability, robustness, and domain adaptation. This project explores integrations of modern LMs with ontologies and knowledge graphs to address these challenges. The project will be driven by real-world domain applications in casting, materials, and manufacturing, developed in close collaboration with domain experts, ensuring both scientific depth and applied impact.
The consortium held the official kick-off meeting for the project. Both the research team and industry partners participated. The participants aligned on the project goals, collaboration model, and next steps, marking a strong start to the joint work.
We are recruiting a PostDoc Researcher in AI: Language Models, Ontologies, and Knowledge Graphs.
The ontology for casting, materials and manufacturing (v 1.0.0)
This ontology provides a formal, machine-interpretable representation of core knowledge in casting, materials behavior, and manufacturing operations. It presents a first step toward building an AI-native semantic infrastructure for the domain.
All the serialization format for the ontology and supporting HTML documentation are openly available here.
From Prompt to Graph: Comparing LLM-Based Information Extraction Strategies in Domain-Specific Ontology Development
ISM 2025: International Conference on Industry of the Future and Smart Manufacturing
Leveraging LLM and KG for Knowledge Transfer in Traditional Material Manufacturing Industry: Experience and Challenges.
SeMatS 2025: The 2nd International Workshop on Semantic Materials Science
Jönköping University
Jönköping AI Lab
Department of Computing
Department of Materials and Manufacturing
Comptech
Skillingaryd, Sweden
CONSID
Jönköping, Sweden
This project is funded by:
Grant No. 20250058
For general inquiries about the project, please contact:
He Tan
Jönköping AI lab
Department of Computing
School of Engineering
Jönköping University