Workshop "Establishing a knowledge graph community in biomedical science"
Workshop Information
Date: 15-19.06.2026
Place: Heidelberg, Im Neuenheimer Feld 205
Background
Many modern biomedical methods benefit from the availability of prior knowledge, for example about genes, proteins, or diseases. Knowledge graphs, i.e., representations of prior knowledge in machine-readable graph form, have become the quasi-standard for storing, manipulating, and sharing biomedical prior knowledge. To meet the needs of broad user communities in generating knowledge graphs, we have developed BioCypher, a modular framework for the creation of knowledge graphs based on ontologies, targeting single cell and spatial omics, microbiomics, metabolomics, and various multi-omics modelling and machine learning methods.
In this workshop, we will learn about knowledge representations, knowledge graphs, ontologies, and data structures, and put this knowledge to practical use with BioCypher. The workshop will also contain a module on information fusion, leveraging OntoWeaver to combine data from different sources and joining this information into one single graph. Further, we will learn how to utilize generative AI to upscale to more complex projects.
Workshop Aim
Learn how to create knowledge graphs from your data and import them into a graph database for further studies.
Research Topics
- Knowledge graphs
- Biomedical science
- Ontologies
- Knowledge extraction
- Information fusion
- Agentic automation
Confirmed speakers
- Jan Baumbach, University of Hamburg, Germany
- Alberto Santos Delgado, Technical University of Denmark, Copenhagen, Denmark
- Tunca Dogan, Hacettepe University, Ankara, Turkey
- Johann Dreo, Institut Pasteur, Paris, France
- Claire Laudy, Thales & Institut Pasteur, Paris, France
- Sebastian Lobentanzer, Helmholtz Munich, Germany
- Steffen Vogler, Bayer AG, Berlin, Germany
- Judith Wodke, Universität Greifswald, Germany
Stay tuned for updates to the workshop program!
Prerequisites
Requirements
Familiarity with ontologies and/or knowledge graphs and/or Python is helpful. You need to bring a laptop with a working Python installation and an IDE such as VSCode.