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Knowledge Graphs: Why the Relationships in Your Data Matter
Most business data is stored in tables, but much of its value lies in the connections between things: customers and products, suppliers and parts, people and projects, accounts and transactions. Relational databases can hold these relationships, but questions that follow chains of connections quickly become slow to run and hard to express. Knowledge graphs model data as entities and relationships from the start, which makes connected questions natural to ask and fast to answer. They are also increasingly used to give AI systems accurate, explainable context.
This masterclass will guide you from the essentials of graph theory through to a working knowledge graph. Taking a set of ordinary CSV files, we design a graph model, load the data into a real graph database, explore it visually and answer questions that would be painful in SQL. We finish by connecting the graph to a large language model, showing how GraphRAG grounds AI answers in real relationships rather than loosely matched text.
Learning Outcomes
By the end of the session, participants will be able to:
- Understand graph fundamentals: Learn the core ideas of graph theory, including nodes, edges, properties, direction and paths, and how a property graph differs from a relational model.
- Build a knowledge graph from CSV: See ordinary tabular data remodelled as entities and relationships and loaded into a graph database, including the modelling decisions that matter most.
- Query connected data: Watch graph queries answer multi-hop questions such as shortest paths, shared connections and hidden links, and compare them with the equivalent SQL.
- Combine graphs with AI: Understand how knowledge graphs improve retrieval-augmented generation (GraphRAG), reduce hallucinations and make AI answers traceable to their sources.
Target Audience
This masterclass is designed for hands-on practitioners, including developers, data engineers, analysts and architects, who work with highly connected data and want a better way to model and query it.
It is also relevant to data leaders and technical managers exploring how to give AI systems reliable, explainable context.
No prior experience of graph databases is needed; familiarity with spreadsheets or SQL will help participants get the most from the demos.
