Computer Science student Kim Mennemann has successfully defended her master’s thesis, marking a significant step forward in making global biodiversity data more accessible and “computable.”

The research, titled “Semantic Modeling of IPBES Assessments - Ontology Design for the Conceptual Framework, Glossaries and Keyword Enrichment,” was supervised by Prof. Dr. Kai Eckert, in collaboration with Dr. Maral Dadvar and Dr. Aidin Niamir from the Senckenberg Biodiversity and Climate Research Institute. Dr. Niamir serves as Head of the Technical Support Unit for Knowledge and Data at IPBES — the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services — the leading global body synthesizing scientific, indigenous, and local knowledge to inform policy decisions on biodiversity and ecosystem management. The thesis was conducted in collaboration with and supported by Senckenberg.

The Challenge: From Narrative to Knowledge Graphs

The IPBES produces critical assessment reports that shape global conservation policy. However, these reports traditionally exist as static text and manual tables, making it difficult for scientists to systematically compare concepts or track how terminology evolves across different documents.

Bridging the Gap with Semantic Technologies

Mennemann’s thesis addresses this by applying Semantic Web technologies to the IPBES framework.

By developing a formal ontology, the research transforms narrative report elements into a machine-readable format.

Key achievements include:

• Digital Framework Modeling: Creating a structural backbone that organizes knowledge across core IPBES fields.
• Glossary Integration: Mapping nearly 1,000 terms across multiple major reports to preserve context and definitions.
• Semantic Enrichment: Linking internal keywords to global external knowledge bases like Wikidata, connecting biodiversity data to the broader “Linked Open Data” ecosystem.

Impact and Future Collaboration

This work directly supports the FAIR principles (Findable, Accessible, Interoperable, and Reusable) and the IPBES Data and Knowledge Management Policy. While previous efforts focused on the structure of reports (chapters and references), this thesis captures the content—the actual concepts and definitions.

We look forward to further collaboration between Mannheim Technical University and Senckenberg, to develop and deploy AI- and knowledge-based systems to support highly relevant biodiversity research.