Building Knowledge Graphs

Chapter 2. Organizing Principles for Building Knowledge Graphs

Graphs are common in modern computer systems. They’re a pleasant and flexible data model for supporting interactive queries, real-time analytics, and data science. But what transforms a graph into a knowledge graph is the application of an organizing principle that helps people and software to understand it. Historically, this has loftily been called semantics, but you can think of it as making the data smarter.

Knowledge graphs are the result of decades of research in semantic computing. With modern graph technology, you can easily apply the fruit of all of that research to contemporary problems.

This chapter introduces common organizing principles for knowledge graphs. Once you’ve finished reading this chapter, you will be able to choose from different organizing principles that best suit the problems you want to solve.

Organizing Principles of a Knowledge Graph

The notion that knowledge graphs help make data smarter is appealing. Rather than having to repeatedly encode smart behavior into applications, it is encoded once, directly into the data. Smarter data enables knowledge reuse and reduces duplication and discrepancies.

There are several different approaches to organizing data in a graph, each with its own benefits and quirks. You’re free to choose the ones that fit your problem, and you’re free to combine approaches as well.

Starting with a basic (but useful) graph, we’ll show how to add successive layers ...