Master Thesis Graph-Based Question Answering and Retrieval-Augmented Generation Systems
Core
Design and implement a prototype GraphRAG system integrating semantic web technologies (RDF, OWL, SPARQL) to improve retrieval quality and answer accuracy for heterogeneous data sets.
Role type
Master Thesis Researcher (GraphRAG & Semantic Web)
Builds
A prototype system combining symbolic reasoning and graph algorithms with large language models.
Domain
Artificial Intelligence, Knowledge Graphs, Semantic Web
Deliverable
production ML models
Required skills
Machine learning, graph data science, semantic web technologies, Python (object-oriented), RDF/OWL/SPARQL, graph algorithms
Preferred skills
RAG systems experience, ontology-driven reasoning
Technologies
RDF, OWL, SPARQL, Python
Responsibilities
Conduct literature review on GraphRAG and knowledge graph question answering; investigate symbolic approaches and graph algorithms; design and implement a prototype system; validate approach using public benchmarks and internal datasets.
Seniority
Master's level research project
