Fusing RDF Knowledge Graphs with Deep Learning for Advanced Recommender Systems

Objective

This project seeks to expand AutoRDF2GML, an open-source framework acclaimed for converting RDF data into specialized representations ideal for cutting-edge graph machine learning (GML) tasks, including graph neural networks (GNNs). With its automatic extraction of both content-based and topology-based features from RDF knowledge graphs, AutoRDF2GML simplifies the process for those new to RDF and SPARQL, making semantic web data more accessible and usable in real-world applications.

What are the tasks?

• Adapt AutoRDF2GML to process a broader range of RDF knowledge graphs, allow a flexible integration of data sources from the Linked Open Data cloud.
• Redesign the AutoRDF2GML interface to be more intuitive and user-friendly, enabling a seamless experience for both new and experienced users.
• Boost the framework’s automation capabilities to simplify the setup and execution processes, making it easier to generate and use graph machine learning datasets efficiently.

What prerequisites do you need?

• Proficiency in Python, with a foundational understanding of RDF, SPARQL, and graph machine learning concepts.
• An enthusiastic interest in the intersection of semantic web technologies and deep learning.

Further Reading

http://autordf2gml.org/

Contact Person

Prof. Dr.-Ing. Michael Färber, michael.faerber@tu-dresden.de