See how SAP HANA Cloud’s Knowledge Graph powers LLM-ready Q&A – grounded in real business context and SAP data.
In this keynote demo for developers, we show how to activate the triple store in SAP HANA Cloud and use the Knowledge Graph engine to model relationships (people, demos, capabilities) as RDF triples, then query them with natural language. The ontology captures who is presenting which demos and which SAP capabilities they cover; SAP HANA stores it as triples and exposes it for SPARQL queries.
To make this practical, we connect a Python package to the same SAP HANA instance, instantiate the graph, and route a plain-English question through an LLM available via SAP AI Core / Generative AI Hub. The model auto-generates SPARQL, executes it against SAP HANA, and returns precise answers (including links, thanks to RDF). This pattern, graph semantics + LLM planning, grounds AI with business context to reduce hallucinations and improve answer quality.
We also touch on how SAP’s broader data fabric makes this stronger: SAP Business Data Cloud innovations and SAP Snowflake (as a solution extension) help keep data where it lives while sharing semantics and governance – so graphs, agents, and analytics can work across your landscape without brittle copies.
Speaker: Witalij Rudnicki, Developer Advocate, SAP
00:03 – Setup: Why use SAP HANA Cloud + SAP Knowledge Graph
00:11 – Activate triple store; load ontology & triples
01:19 – Connect via Python; instantiate the graph
01:42 – Use SAP AI Core LLM to generate SPARQL from natural language
02:23 – Answers returned; RDF links to web resources; wrap
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