Language Agnostic Semantic Search driven by Knowledge Graphs
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
We are looking for a great people starting as soon as possible
- 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.
- 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.
alpe@campus.uni-paderborn.de
Fachgruppe Data Science (EIM-I)
Warburger Str.
100
33098 Paderborn