Ontologies from Natural Language Expert Knowledge

  • Type:Master Thesis
  • Supervisor:

    Michelle Jungmann

    Prof. Sanja Lazarova-Molnar

Description

Problem: Expert knowledge is very valuable for various processes in companies, however, often boxed in natural language. Ontologies and Knowledge Graphs have the potential to structure and provide valuable expert knowledge to digital twins. However, the automated integration with data-driven digital twins is still lacking.

Goal: The goal of this thesis is to research ontologies and knowledge graphs in the area of digital twins and beyond. Based on the literature review and our current research the goal is to develop a proof of concept knowledge graph from natural language expert knowledge and propose an integration into a digital twin model.

Required Skills and Knowledge:

  • Programming proficiency (preferably in Phyton)
  • Basic skills in digital twins 
  • Basic skills in Petri nets
  • Basic skills in Ontologies and Knowledge Graphs