Scaling up semantics; lessons learned across the life sciences

Semantic modeling is key to understanding the biological processes underpinning the health of humans and the health of ecosystems on this planet. There are a number of different approaches to semantic modeling, varying from modeling of *things* in the form of knowledge graphs, modeling of *data structures* in the form of semantic schemas, and modeling of *words* in the form of ultra-large language models. Taking the metaphor of modeling paradigms as planets in a semantic solar system, I will take us on a tour through the solar system, exploring the strengths of each approach, and looking through a historic lens at how we keep iterating over similar solutions with each rotation around the sun. As an alternative to the dichotomy of either resisting change, or starting afresh I urge an approach were we embrace change and adapt with each revolution. I will look specifically at how the OBO community have built powerful knowledge graphs of biological concepts, how the LinkML modeling language incorporates aspects of both frame languages and shape languages, and how language models can be integrated with semantic ontological approaches through the OntoGPT framework