1 citations · 1 across the 6 of their papers we have counts for
6 papers
NeSy-RAG: Neuro-Symbolic RAG for Explainable Question Answering
Jonas Gann, Michael Gertz
Retrieval-augmented generation (RAG) improves question answering by grounding large language models (LLMs) in external knowledge such as text corpora. However, its reasoning proces…
Exploring Drug Safety Through Knowledge Graphs: Protein Kinase Inhibitors as a Case Study
David Jackson, Michael Gertz, Jürgen Hesser
Adverse Drug Reactions (ADRs) are a leading cause of morbidity and mortality. Existing prediction methods rely mainly on chemical similarity, machine learning on structured databas…
From Answers to Guidance: A Proactive Dialogue System for Legal Documents
Ashish Chouhan, Michael Gertz
The accessibility of legal information remains a constant challenge, particularly for laypersons seeking to understand and apply complex institutional texts. While the European Uni…
heiDS at ArchEHR-QA 2025: From Fixed-k to Query-dependent-k for Retrieval Augmented Generation
Ashish Chouhan, Michael Gertz
This paper presents the approach of our team called heiDS for the ArchEHR-QA 2025 shared task. A pipeline using a retrieval augmented generation (RAG) framework is designed to gene…
ClusterChat: Multi-Feature Search for Corpus Exploration
Ashish Chouhan, Saifeldin Mandour, Michael Gertz
Exploring large-scale text corpora presents a significant challenge in biomedical, finance, and legal domains, where vast amounts of documents are continuously published. Tradition…
LexDrafter: Terminology Drafting for Legislative Documents using Retrieval Augmented Generation
Ashish Chouhan, Michael Gertz
With the increase in legislative documents at the EU, the number of new terms and their definitions is increasing as well. As per the Joint Practical Guide of the European Parliame…