Building Schema-Free Applications with RDF
Session Abstract
RDF was designed for the semantic web, but it turns out to be a perfect fit for systems where structure emerges from user interaction, not upfront design. This talk covers how to build applications entirely on RDF triples, translate natural language to SPARQL with small, open source language models, and discover implicit knowledge in user input.
Session Description
Most applications assume their data model is known before the first user interacts with the system. But there are cases where this assumption doesn’t hold, and the structure of the data needs to emerge from how people use the system rather than being designed upfront.
This talk explores why common database paradigms fall short for this use case and how that search led us to Resource Description Framework (RDF). Originally designed for the semantic web, RDF stores knowledge as subject-predicate-object triples, a surprisingly natural fit for application data when the schema isn’t fixed.
We cover the practical side: using fine-tuned open source models to translate natural language into SPARQL queries, drawing on research like FIRESPARQL, storing data with tools like Oxigraph, and self-hosting models with our open source model serving platform Paddler (https://github.com/intentee/paddler).
Finally, we show how LLMs can derive not just what users explicitly say but also implicit relationships, opening new possibilities for analytics and knowledge discovery.
