Neurosymbolic integration
We develop neurosymbolic integration both at the conceptual level as well as in several application areas, including chemistry, material science and human behaviour. We use well-developed ontological background knowledge in order to enhance the performance of machine learning methods, e.g. by using a semantic loss function or neuro-fuzzy rules. We have developed a novel neurosymbolic architecture for ontological classification of structured entities and of ontology extension.
Current projects
- StrOntEx (Ontology Extension by Automated Learning and Reasoning from Structured Entities) funded by DFG
Completed projects