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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.

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Master thesis project

SINDIT covers aspects related to Digital Twin data representation (graph-based data structures for assets data and processes), data storage (assets and time-series data), and support for discrete/continuous simulation (e.g., estimation of production capacity).

The purpose of the framework is to provide the mechanisms to represent and store data in a way that can capture assets and time-series data, while at the same time can offer efficient access to the data and ability to use the stored data for various types of simulations (discrete/flow).

Extending Digital Twins with multimodal data, i.e., incorporating various data types such sensor data, images, text and geospatial data can significantly enhance their capabilities. One approach is to use Knowledge Graphs (KGs) to represent and integrate this multimodal data.

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

Research topic focus

  • Extend the SINDIT framework with support for Multimodal Knowledge Graph integration. 
  • Automate AI Model deployment within SINDIT.

Expected results and learning outcome

After the thesis is successfully submitted, the student should have a better understanding and practical experience working with Multimodal Knowledge Graphs,  Digital Twin technologies and related Python libraries.

Qualifications

Candidates should have good understanding of data engineering, semantic technologies and digital twin technologies. Moreover, programming experience with Python and related data processing libraries is recommended as the SINDIT framework is developed in Python.

References