Knowledge Graphs

Chapter 4. Data Processing for Driving Decisions

Graphs provide context to answer questions, improve predictions, and suggest best next actions. But uncovering insight from graph data is a necessary step toward unleashing value.

In the actioning knowledge graphs we saw in Chapter 3, an organizing principle was applied to an underlying graph in order to extract knowledge. We said this makes the data smarter. Deciding upon or discovering an organizing principle, or even just exploring the graph to find its general properties, is a useful activity in its own right.

In this chapter, we’re going to explore decisioning knowledge graphs. A decisioning knowledge graph does not drive actions directly but surfaces trends in the data, which can be used in several ways such as to extract a view or subgraph for:

• Specific analyses (e.g., monopartite graphs like customer-bought-product) yielding actionable knowledge that can be written back into an actioning knowledge graph

• Human analysis (assisted by tooling) for data science exploration and experimentation, eventually possibly yielding insight that is written to the actioning knowledge graph or influences organizational structure

• Further processing by downstream systems (e.g., training machine-learning models)

Physically, our decisioning graph might or might not be the same graph as our actioning knowledge graph. Sometimes it’s helpful to keep all the actionable data and decision making together (particularly when we want to enrich ...