There are a huge number of knowledge graphs available for AI-related applications such as smart analysis, link prediction, and recommendation. We focus on three major domains of Environmental, Medical or Scholarly domains and their available datasets. We plan to use embedding models in order to provide smart analytics and link prediction services on them. The student will start with exploration of relevant knowledge graphs (a two weeks task) and is expected to fully master understanding the structure of the data, entities and relations and data dependencies.

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  • Excellent skills in working with data in different formats (RDF, CSV)
  • Excellent skills in programming with Python, PyTorch (Java and other languages are extra helpful)
  • A basic and good understanding of embedding models and the role of Score and Loss functions

(Before contacting please read this paper: RotatE and understand its implementation).

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