Building The LinkedIn Knowledge Graph

LinkedIn's knowledge graph is a large knowledge base built upon "entities" on LinkedIn, such as members, jobs, titles, skills, companies, geographical locations, schools, etc. To solve the challenges we face when building the LinkedIn knowledge graph, we apply machine learning techniques, which is essentially a process of data standardization on user-generated content and external data sources, in which machine learning is applied to entity taxonomy construction, entity relationship inference, data representation for downstream data consumers, insight extraction from graph, and interactive data acquisition from users to validate our inference and collect training data. By mining member profiles for entity candidates and utilizing external data sources and human validations to enrich candidate attributes, we created tens of thousands of skills, titles, geographical locations, companies, certificates, etc., to which we can map members. Given the power-law nature of the member coverage of entities, linguistic experts at LinkedIn manually translate the top entities with high member coverages into international languages to achieve high precision, and PSCFG-based machine translation models are applied to automatically translate long-tail entities to achieve high recall.