At LinkedIn, we use machine learning technology widely to optimize our products: for instance, ranking search results, advertisements, and updates in the news feed, or recommending people, jobs, articles, and learning opportunities to members. An important component of this technology stack is a knowledge graph that provides input signals to machine learning models and data insight pipelines to power LinkedIn products. This post gives an overview of how we build this knowledge graph.
WHY THIS IS IMPORTANT
LinkedIn has 450M members, 190M historical job listings, 9M companies, 200+ countries (where 60+ have granular geolocational data), 35K skills in 19 languages, 28K schools, 1.5K fields of study, 600+ degrees, 24K titles in 19 languages, and 500+ certificates, among other entities. Making sense of relations between those entities is a difficult task and this paper explains how LinkedIn does it. Not for the technically faint of heart.