09/08/2026 –, TR410
Many of us know SPY×FAMILY — a “family” where each member has a hidden identity. At first, it looks like a simple family story, but behind it is a network of hidden relationships.
What if we could visualize such hidden relationships in knowledge?
In the open-source world, we have access to a huge amount of unstructured data: Wikipedia, documents, and web pages. These sources are easy to read, but difficult to reuse, connect, and track as their content changes over time. Relationships may be hidden in text, but their importance, context, and changes over time are even harder to see.
The core of this session is a step-by-step demonstration of how to turn a Wikipedia page into a knowledge graph using Neo4j and the Neo4j LLM Knowledge Graph Builder. The demonstration uses LLMs to extract entities and relationships from the Wikipedia page, and stores the extracted knowledge as a graph that can be explored, compared, and reused.
Then we will pick two versions of the same Wikipedia page — past and present — and compare them as graphs. This allows us to see how knowledge evolves: what was added, what changed, and how relationships grow over time.
This session is for developers, data engineers, and open-source contributors who want to build their own knowledge graph environment. By following the steps, participants will learn how to start from open data, build a graph, compare versions, and apply the same approach to their own documents or web content.
This talk is not only about building graphs. It is about exploring knowledge, comparing it, and understanding how it grows.
Everything in this session is based on open data and open tools. No special dataset is required — just use the data already around you.
Koji Annoura is a Knowledge Graph architect and open-source community leader based in Japan.
He has been active in open-source communities for over 20 years, organizing developer meetups and user groups such as the Neo4j Users Group Tokyo and the Apache Hop User Group Japan.
His work focuses on graph technologies, knowledge design, SQL/PGQ, GQL, and Context Graphs for connecting knowledge, evidence, decisions, and time.
He regularly speaks at international conferences and shares reproducible, hands-on approaches for building knowledge graphs, traceable knowledge systems, GraphRAG systems, and open knowledge workflows.