RDF2Vec has emerged as a popular method for unsupervised feature extraction from RDF graphs. However, RDF2Vec cannot handle large graphs efficiently, due to (i) the size of RDF graphs or intermediate results being prohibitively large, and (ii) the high computational complexity associated with graph walks of increasing breadth and depth, which makes their processing difficult, if not impossible, on a single machine. We address this limitation by introducing SERE, a scalable and distributed framework for unsupervised embeddings computation on large–scale Knowledge Graphs. SERE is open–source, well–documented, and fully integrated into the SparkKG–ML Python library. Our experiments demonstrate that SEREis able to compute embeddings over Knowledge Graphs with millions of edges within hours, and is up to 7 times faster than RDF2Vec even for small Knowledge Graphs, all while achieving comparable accuracy to RDF2Vec in a benchmark classification task.

People increasingly use the Internet to make food-related choices, prompting research on food recommendation systems. Recently, works that incorporate nutritional constraints into the recommendation process have been proposed to promote healthier recipes. Ingredient substitution is also used, particularly by people motivated to reduce the intake of a specific nutrient or in order to avoid a particular category of ingredients due for instance to allergies. This study takes a complementary approach towards empowering people to make healthier food choices by simplifying the process of identifying plausible recipe substitutions. To achieve this goal, this work constructs a large-scale network of similar recipes, and analyzes this network to reveal interesting properties that have important implications to the development of food recommendation systems.