Building Knowledge Graphs and Recommender Systems for Suggesting Reskilling and Upskilling Options from the Web

• Published in: Information (Basel) 2022-11, Vol.13 (11), p.510
• Main Authors: Weichselbraun, Albert, Waldvogel, Roger, Fraefel, Andreas, van Schie, Alexander, Kuntschik, Philipp
• Format: Article
• Language: English
• Subjects: Automation; Careers; Continuing education; Datasets; Deep learning; Education; Employees; entity classification; entity linking; entity recognition; Graph theory; knowledge base population; knowledge extraction; Knowledge representation; Labor market; Linked Data; Open data; Population; Recommender systems; Semantic web; Semantics; Skills; slot filling; Technology adoption; Websites
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info13110510

As advances in science and technology, crisis, and increased competition impact labor markets, reskilling and upskilling programs emerged to mitigate their effects. Since information on continuing education is highly distributed across websites, choosing career paths and suitable upskilling options is currently considered a challenging and cumbersome task. This article, therefore, introduces a method for building a comprehensive knowledge graph from the education providers’ Web pages. We collect educational programs from 488 providers and leverage entity recognition and entity linking methods in conjunction with contextualization to extract knowledge on entities such as prerequisites, skills, learning objectives, and course content. Slot filling then integrates these entities into an extensive knowledge graph that contains close to 74,000 nodes and over 734,000 edges. A recommender system leverages the created graph, and background knowledge on occupations to provide a career path and upskilling suggestions. Finally, we evaluate the knowledge extraction approach on the CareerCoach 2022 gold standard and draw upon domain experts for judging the career paths and upskilling suggestions provided by the recommender system.