Knowledge Graphs

Chapter 2. 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 human and software users to understand it. Sometimes this is loftily called semantics, but we just think of it as making the data smarter.

Knowledge graphs are the result of decades of research in semantic computing but with modern graph technology, we can modernize and generalize that research so that it can be applied to contemporary real-world problems. In this chapter, we introduce common organizing principles for knowledge graphs—how to add metadata to a graph to make it smarter. Once you’ve finished reading this chapter, you will be able to choose from different organizing principles that best suit your predicament.

Organizing Principles of a Knowledge Graph

We like the notion that knowledge graphs help make data smarter. Rather than having to repeatedly encode smart behavior into applications, we encode it once, directly into the data. Smarter data benefits 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. We’re free to pick and choose the ones that are best suited to our problem, and we’re free to compose them together too. Starting with a basic (but ...