Building Knowledge Graphs

Chapter 6. Enriching Knowledge Graphs with Data Science

This chapter will introduce you to graph data science. The aim of graph data science is to gain insight into your knowledge graph using graph algorithms. Towards that goal, you’ll learn about the common types of graph algorithms and the insights they unearth as well as how Neo4j Graph Data Science provides a simple platform for experimentating with, sharing, and productizing graph algorithms. You’ll also learn how to execute graph algorithms on real knowledge graphs and experience how the system does much of the heavy lifting for you.

Like Chapter 3, this chapter is not intended to be comprehensive but rather to give you the foundation to be able to work through the remainder of the book. For those looking for more depth, there are many good books dedicated to specific deep technical topics, like Graph Data Science for Dummies by Dr. Alicia Frame and Zach Blumenfeld (Wiley).

Why Graph Algorithms?

Graph algorithms yield some insight about a knowledge graph’s structure. That insight could be influential people in a social graph, critical junctions in a rail network, cells of fraudsters, or a common pathogen in a disease pathway. While the design and implementation of graph algorithms is a specialist subject, using them is not. You simply need to understand the purpose of the algorithms and the syntax to execute them over your knowledge graph.

Note

As in Chapter 3 Neo4j has been chosen for graph algorithm examples. It has the ...