# Apache Jena Projects at AI Tinkerers

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> Markdown URL: https://aitinkerers.org/technologies/apache-jena.md
> Technology record last updated: 2026-03-11T16:51:46Z
> Generated: 2026-08-24T18:52:19Z

A robust Java framework for building Semantic Web and Linked Data applications using RDF, SPARQL, and OWL.

Apache Jena provides a complete stack for graph-based data modeling. It includes the TDB triple store for high-performance persistence, the Fuseki server for SPARQL over HTTP, and a comprehensive API for manipulating RDF graphs. Developers use Jena to ingest data in Turtle or JSON-LD formats and apply RDFS or OWL inference rules to uncover hidden relationships. It is the industry standard for enterprise knowledge graphs (EKGs) requiring strict W3C compliance and scalable query execution.

- Official technology site: https://jena.apache.org/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [GraphRAG in action: solving real-life use cases with intelligent retrieval and generation](https://poland.aitinkerers.org/talks/rsvp_4FU8Sw9rrJ4)

RAG serves as a prime example of a practical implementation of Generative AI, with clear ROI and proven use cases widely documented online. However, when you dive deeper and attempt to solve real-world problems, the process is often more complex than it initially appears. Off-the-shelf solutions frequently fall short, and in this talk, I’ll explain why that’s the case. As a more robust alternative, I’ll introduce a powerful approach to intelligent RAG: the GraphRAG framework, applied specifically to customer service tickets. This presents an intriguing challenge because, as in real-life scenarios, we lack a well-structured knowledge base. Instead, we must build one from existing email correspondence and resolved tickets. Traditional methods for managing such documents often struggle to maintain relevance and deliver contextually accurate answers. In this session, we’ll explore how GraphRAG—a hybrid approach that combines graph-based retrieval and generative models—can enhance customer service workflows. Through a hands-on demonstration, I’ll highlight where GraphRAG excels, its limitations, and the practical lessons learned. Ultimately, I aim to answer a question I once had: Is GraphRAG truly worthwhile, and what is its tangible impact on real-world use cases, beyond carefully curated examples?

- Event context: AI Tinkerers Poland - Second Meetup in Warsaw (January) — 2025-01-30 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_4FU8Sw9rrJ4

## Related Technologies

- [ArangoDB](https://aitinkerers.org/technologies/arangodb) ([Markdown](https://aitinkerers.org/technologies/arangodb.md)) — 2 public demos
- [Blazegraph](https://aitinkerers.org/technologies/blazegraph) ([Markdown](https://aitinkerers.org/technologies/blazegraph.md)) — 2 public demos
- [Dgraph](https://aitinkerers.org/technologies/dgraph) ([Markdown](https://aitinkerers.org/technologies/dgraph.md)) — 1 public demo
- [Generative models](https://aitinkerers.org/technologies/generative-models) ([Markdown](https://aitinkerers.org/technologies/generative-models.md)) — 3 public demos
- [GraphRAG](https://aitinkerers.org/technologies/graphrag) ([Markdown](https://aitinkerers.org/technologies/graphrag.md)) — 13 public demos
- [Neo4j](https://aitinkerers.org/technologies/neo4j) ([Markdown](https://aitinkerers.org/technologies/neo4j.md)) — 29 public demos
- [RAG](https://aitinkerers.org/technologies/rag) ([Markdown](https://aitinkerers.org/technologies/rag.md)) — 146 public demos
- [RDF](https://aitinkerers.org/technologies/rdf) ([Markdown](https://aitinkerers.org/technologies/rdf.md)) — 2 public demos
- [SPARQL](https://aitinkerers.org/technologies/sparql) ([Markdown](https://aitinkerers.org/technologies/sparql.md)) — 2 public demos
- [TypeDB](https://aitinkerers.org/technologies/typedb) ([Markdown](https://aitinkerers.org/technologies/typedb.md)) — 1 public demo