Data Interoperability, Knowledge Graphs & Digital Twins

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 aim of this thesis project is two-fold: (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, energy grid infrastructures or container vessels, and complex ecosystems such as the Norwegian freshwaters and continental shelf.

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 Oslofjord is dying. There is a lot of monitoring equipment and models out there. However, they are not integrated for a holistic view. The objective of this thesis is to implement a proof-of-concept Digital Twin of the Oslofjord. There should be a frontend (map with layers) and a backend (to connect to various services like ferryboxes, satellite imagery, underwtater cameras, sensors, etc.)

7. GeoAI for Environmental Twins 

We need more intelligent geospatial data solutions for our environmental twin projects. GeoAI can provide solutions for understanding environmental impact of specific types of climate events, such as severe flooding or sewage dumps into fjords. The objective 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. Development of Integration of Language-Agnostic Streaming Operators to Enable GNNs-ready Stream processing

Given the mounting importance of Graph Neural Networks (GNNs) across a myriad of fields, a palpable challenge arises: integrating these networks into prevalent frameworks. This master's thesis is primed to tackle this challenge, with aims to pioneer the embedding of leading GNN libraries, irrespective of their programming languages, into the Magma Java-based stream processing framework.