From SPY×FAMILY to Evolving Knowledge Graphs: Tracking Wikipedia Changes with Neo4j and LLMs

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.