Graph Database Sydney

In the evolving landscape of AI, Generative AI (GenAI) and Knowledge Graphs (KGs) are emerging as a powerful duo, transforming how machines understand, generate, and reason with information. This meetup explores the seamless integration of structured knowledge from graphs with the creativity and adaptability of GenAI, unlocking new possibilities for intelligent applications.

The agenda of the evening would be:
- 5pm: doors open
- 5.30pm: announcements and welcome
- 5.40pm: 1st Talk
- 6.10pm: 2nd Talk
- 6:40pm: Networking
- 7.10pm: doors close

Speakers:
🎤 Paul King, Distinguished Engineer at Object Computing, VP at Apache Groovy
Talk topic : Using Graph Databases with Groovy
Description : This talk looks at a common case study using Apache TinkerPop, Neo4j, Apache AGE, OrientDB, ArcadeDB, Apache HugeGraph, TuGraph, and GraphQL. It looks at the current state of play of query languages and JVM language support. As a bonus, we get to relive some Australian Olympic swimming records!
Bio : Dr Paul King is a software engineer and computer scientist. He has
been contributing to open source projects for over 30 years and is an
active committer on numerous projects including Groovy, GPars, Grails,
Geb, and Gradle. Paul is VP Apache Groovy, speaks at international
conferences, publishes in software magazines and journals, and is a
co-author of Manning’s best-seller: Groovy in Action, 2nd Edition.

🎤 Fabio henrique Soares, Founder, Solo AI
Talk topic : Connecting the Dots: What a Podcast Can Teach Us About Knowledge Graphs
Description : Podcasts are more than just conversations—they're gateways to deeper learning. In this talk, you'll see how we transformed Naruhodo, a Brazilian science podcast, into an interactive knowledge graph using Neo4j. By scraping and structuring the rich set of references shared in each episode—spanning scientific articles, books, and online resources—we connected episodes through shared themes and sources. This graph-based approach not only reveals hidden connections but also enables powerful applications, from personalized content recommendations to semantic exploration and thematic clustering.
Bio : I am an AI engineer-in-training with a background in data analytics and a Master’s degree from the Technological University of Shannon, where my thesis focused on recommendation systems using Graph Neural Networks (SageConv). I’ve since taken courses on Neo4j & LLM Fundamentals and Graph Data Modeling (Neo4j), and gained experience building AI solutions in Python.
Before transitioning to AI, I spent three years in maintenance engineering in the automotive industry, honing my problem-solving and teamwork skills. I am also passionate about sustainability and constantly seek data-driven ways to create a positive impact.
Founder of Solo AI, an AI agency focused on developing solutions that simplify everyday tasks for individuals and organizations.