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

Abstract

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.

Anticipating Job Market Demands—A Deep Learning Approach to Determining the Future Readiness of Professional Skills

“…Future work will integrate insights into a skill's future readiness with systems such as CareerCoach [41], which provides reskilling and upskilling suggestions. We also plan to leverage additional background knowledge in the classification process.…”

“…Khaouja et al [8] distinguish three different levels of skill label granularity: words (e.g., "Java"), multi-word phrases (e.g., "Java Programming"), and sentences (e.g., "Experience in software development, particularly the development of Java Web applications."). The presented approach draws upon German bipartite skill labels that are part of a proprietary occupation knowledge base which formalizes domain knowledge [41] in the human resources domain. These labels are multi-word but limited to skill topic and verb (e.g., Java [topic] Programming [verb] ) and do not convey any additional context information.…”