Keeping Up with the Market: Extracting competencies from Norwegian job listings

The Norwegian labour market is under continuous change because of fast-paced innovation in technology. It is therefore vital for educational institutions curricula to reflect the changing requirements to keep the population hireable and provide employers with a highly adaptable workforce. There are no complete systems that let us analyse and extract this information about the labour market efficiently. Therefore, there is a need for tools to keep up with the labour market changes and to enable efficient analysis on large Norwegian job listing data sets. In this project, we developed an algorithm that extracts the skills, competencies and knowledge from Norwegian job listing data. Our evaluation results show that we manage to extract skills from the jobs listings, but not to the extent of our defined requirements. This is caused by language ambiguity and semantic differences between our data sets, which significantly impacted our results. We conclude that our algorithm has not fully solved the complex problem at hand but that our project has contributed with open-source code and processed open access data sets. Furthermore, through the development of the algorithm and analysis of the data sets, we have laid the foundation for future work and proposed how to develop solutions for understanding the fast-paced and continuous change in the Norwegian labour markets

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure