5 Knowledge graph learning
This chapter covers
- Building and working with knowledge graphs
- Implementing open information extraction to generate knowledge graphs from text
- Using semantic knowledge graphs for query expansion and rewriting and arbitrary relationship discovery
- Interpreting documents with semantic knowledge graphs to power content-based document recommendations
In the last chapter, we primarily focused on learning relations between queries and documents based on user behavioral signals. In Chapter 2, we also discussed how textual document content, rather than being "unstructured data", is more like a giant graph of hyper-structured data containing a rich graph of semantic relationships connecting the many character sequences, terms, and phrases that exist across our collections of documents.
In this chapter, we demonstrate how to leverage this giant graph of semantic relationships within our content to better interpret your domain-specific terminology. We accomplish this using both traditional knowledge graphs, which enable explicit modeling of relationships within a domain, and semantic knowledge graphs, which enable real-time inference of nuanced semantic relationships within a domain.