A major part of the job offers and candidates profiles are now available online. Leveraging this public data, Multiposting, a subsidiary of SAP, aims at providing in realtime an exhaustive job market analysis through the SmartSearch project. One big issue in this project, and more generally in the e-recruitment and the human resources management, is to extract the skills from the raw texts in order to associate a job or a candidate to its corresponding skills. This paper proposes to generate a multilingual base of skills in a novel bottomup approach that finds its roots from the terminology used by candidates in professional social networks. The knowledge base is built by leveraging the Linked Open Data project DBpedia, as well as the tags of a QandA website, StackOverflow. The large-scale experiments on real-world job offers show that the coverage and precision of the skills extraction are higher using this base than existing bases. The system has been implemented in industrial context and is used daily to extract the skills from thousands of documents, leading to advanced statistics as illustrated at the end this paper.