Chapter 3. Graph Databases
In our book Knowledge Graphs: Data in Context for Responsive Businesses (O’Reilly), we said that knowledge graphs predate modern graph data technology. In fact, the concept of knowledge graphs appeared alongside the Semantic Web, before the term graph database had been coined.
More recently, graph databases and graph processing have become a significant trend in contemporary business systems. We’ve observed a correlation between the renewed interest in knowledge graphs and the widespread emergence of graph databases and graph data science tools. In a sense, modern graph technology has put knowledge graphs within practical reach of many organizations, rather than being mostly confined to academia.
This chapter will introduce you to graph databases at a basic practitioner level. It shows you how to use a graph database and, in particular, how to use a query language to store and query knowledge graphs. It also shows you how graph databases work and why they are significantly more performant for knowledge graph workloads than other data technologies.
Tip
The most popular graph database in terms of its maturity and reach is Neo4j, and all of the technical examples in this and subsequent chapters are based on the Neo4j graph database and associated tooling.1 Neo4j is freely available from https://neo4j.com. The easiest way to run Neo4j on your computer is via the Neo4j desktop app. If you’d prefer not to install Neo4j, then consider Neo4j Sandbox and Neo4j ...
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