This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode.

Introduction.- From Word Embeddings to Knowledge Graph Embeddings.- RDF2vec Variants and Representations.- Tweaking RDF2vec.- RDF2vec at Scale.- Example Applications beyond Node Classification.- Link Prediction in Knowledge Graphs (and its Relation to RDF2vec).- Future Directions for RDF2vec.