11 citations · 14 across the 4 of their papers we have counts for
5 papers
Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI
Nishtha N. Vaidya, Stephan Grimm, Thomas Hubauer +1
Large language models (LLMs) increasingly underpin scientific AI applications that reason over structured knowledge, from biomedical question answering to materials informatics. Ho…
Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods
Anna Himmelhuber, Dominik Dold, Stephan Grimm +2
Machine learning (ML) on graph-structured data has recently received deepened interest in the context of intrusion detection in the cybersecurity domain. Due to the increasing amou…
A Reference Model for Common Understanding of Capabilities and Skills in Manufacturing
Aljosha Köcher, Alexander Belyaev, Jesko Hermann +7
In manufacturing, many use cases of Industry 4.0 require vendor-neutral and machine-readable information models to describe, implement and execute resource functions. Such models h…
Combining Sub-Symbolic and Symbolic Methods for Explainability
Anna Himmelhuber, Stephan Grimm, Sonja Zillner +3
Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed to provide…
Ontology-Based Skill Description Learning for Flexible Production Systems
Anna Himmelhuber, Stephan Grimm, Thomas Runkler +1
The increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing ma…