SkillExtract

Future working life will be increasingly characterized by project work, freelancing and knowledge intensity. This places high demands on dynamic operational skills and knowledge management. According to Deloitte (2016), HR departments are therefore increasingly opening up to cross-company cloud offerings in the area of people analytics and the market is facing exponential growth in the billions worldwide. Companies are increasingly turning to technologies that enable them to generate precise expert and project profiles from unstructured text data, e.g. from social networks, internal wikis or project management systems, which record specialist skills, make it easier to staff projects and find contact persons and uncover operational development potential. So far, however, skills have been recognized as atomic, technical skills, e.g. a programming language or development methodology. However, this means that important contextual information is lost, such as the industry in which someone has used a particular technology.

To address this problem, the "SkillExtract" project aims to develop an algorithm that is able to identify and classify possible relationships between skills. This should make it possible to recognize the application of technical skills, e.g. in certain industries, or the combination of skills, e.g. "Development of an API for Amazon Cloud Services".

• smarTransfer GmbH, Dr. René Wegener
• University of Kassel, Department of Information Systems

Federal Ministry of Education and Research

This project (HA project no.: 628/18-51) is funded as part of Hessen ModellProjekte with funds from LOEWE - Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz, Förderlinie 3: KMU-Verbundvorhaben.

• Prof. Dr. Jan Marco Leimeister
• Dr. René Wegener
• Dr. Philipp Ebel

QuAALi