Language Agnostic Semantic Search driven by Knowledge Graphs


Goal of the Project

We aim at developing efficient and language-agnostic methods for semantic search over knowledge graphs

- Is it possible to provide nearly equal search quality for a large set of different languages?
- What are efficient ways to adapt an out-of-the-box semantic search system to a new language?

Research and Technical Objectives

The methods developed in this project will be provided as use cases based on the products of Springer Nature:
- Springer Materials
- Springer Experiments.

Industry Applications

- Python, Java, SPARQL
- Stardog, Virtuoso, Neo4j
- Docker, Gitlab CI/CD, Github Actions
- Deep Learning for NLP, Semantic Web

Technology Stack

This project is a part of the Software Campus program.


We are looking for a great people starting as soon as possible

Student Assistant

- This position is for bachelor and master students (SHK/WHB with 10h/week).
- Development and research in the field of Question Answering over Knowledge Graphs.

Research Assistant

- Full-time position for 12 months or half-time position for 24 months (both are in salary group E13 TV-L).
- Research and development in the field of Question Answering over Knowledge Graphs.




Fachgruppe Data Science (EIM-I)
Warburger Str. 100
33098 Paderborn