From Knowledge Graphs to Digital Twins: Are Our Models Ready?
KGC 2026
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37m
https://drive.google.com/file/d/1db06jIurJyQDtDwgCOfCLAuy3E_eB3gV/view?usp=share_link
The life sciences community is adopting emerging modeling approaches—LinkML, SHACL-centric design, and updated standards like RDF 1.2—while simultaneously pushing toward ambitious paradigms such as digital twins for precision healthcare. These systems require the integration of heterogeneous data across clinical, genomic, and environmental domains, alongside strict requirements for trust, identity, privacy, and security.
But are our current semantic and data modeling approaches sufficient to support these demands in practice?
This panel will introduce and defend contrasting perspectives grounded in real-world implementations. We will explore trade-offs between schema-first and ontology-driven design, validation versus reasoning, and standardization versus local pragmatism—under the pressure of emerging AI and digital twin use cases.
The goal is not consensus, but clarity: what actually works, what breaks in practice, and what a coherent modeling stack for life sciences could look like going forward.
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# Why GraphRAG? (Intro)
## 1.1 State of the art
State of the art alternatives - strengths and limitations of LLMs, Vectors, FTS, LPG, etc.Demo: Comparison of different retrieval outcomes, using a real-world dataset.
Highlighting wher...
