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Data Interoperability, Knowledge Graphs & Digital Twins

Data-driven decisions affect us all, as more and more data is being generated in industry, smart cities or the natural environment. How can we organize this multitude of data sources in innovative ways to gain new insights, necessary for a more sustainable future? In SFI NorwAI, we offer projects in collaboration with industrial partners on topics such as Data Interoperability, Knowledge Graphs and Digital Twins.

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These projects can be adapted to students from several departments such as Computer Science, Mathematical Sciences or Engineering Cybernetics. The student will need a supervisor associated with the university, but SINTEF can provide most of the practical supervision.

For more information about specific topics please visit the following pages:

1. AI symbolic for enhancing reasoning and trustworthiness of GPT

The goal of this thesis is to explore the use of symbolic AI (e.g., knowledge graphs) to enhance reasoning capabilities of GPT and reduce hallucinations and opaqueness, and improve trustworthiness.

2. Applying ChatGPT for Data Integration

The goal of the thesis is to explore the applicability of ChatGPT to enable a higher degree of automation for data integration in real national and European industrial/research projects.

3. Digital System Models – Implementation and application

The goal of this thesis is to (a) reduce implementation costs of building digital system models using deep learning and semantic technologies, and (b) ensure digital systems models enable domain experts (e.g., engineers and researchers) to better monitor and optimize the performances of real complex systems such as energy production facilities and energy grid infrastructures.

4. Enhancing Data Harmonization with LLMs

The goal of this thesis is to explore the use of Large Language Models (LLMs) to enhance data harmonization related to Knowledge Graphs (KGs).

5. Multimodal Knowledge Graph for Digital Twins

The goal of the thesis is to extend the SINTEF Digital Twin (SINDIT) framework with support for Multimodal Knowledge Graph (KG) integration.

6. Towards a Digital Twin of the Oslofjord

The goal of this thesis is to implement a proof-of-concept Digital Twin of the Oslofjord comprising a frontend (map with layers) and a backend (to connect to various services such as ferryboxes, satellite imagery, underwater cameras and sensors).

7. GeoAI for Environmental Twins 

The goal of this thesis is to implement various GeoAI solutions for specific problem areas such as (1) curation and quality management of geospatial data, (2) automated annotation in geospatial data, and (3) automate analytics workflows.

8. Language-Agnostic Streaming Operators to Enable GNNs-ready Stream processing

The goal of this thesis is to embed leading Graph Neural Networks (GNNs), irrespective of their programming languages, into the Magma Java-based stream processing framework.