  The labor market is constantly evolving. Occupations are changing, being
added, or disappearing to fit the needs of today's market. In recent years the
pace of this change has accelerated, due to factors such as globalization,
digitization, and the shift to working from home. Different factors are
relevant when selecting employment, e.g., cultural fit, compensation, provided
degree of freedom. To successfully fulfill an occupation the gap between
required (by the job) and possessed (by the job seeker) skills needs to be as
small as possible. Decreasing this skill-gap improves the fit between a job
candidate and occupation. In this paper we propose a custom-built Skills &
Occupation Knowledge Graph (KG) that fits the above described dynamic nature of
the labor market, by leveraging existing skills and occupation taxonomies
enriched with external job posting data. We leverage this KG and explore
several applications for skills-based matching of jobs to job seekers. First,
we study link prediction as a means to quantify relevance of skills to
occupations, which can help in prioritizing learning and development of
employees. Next, we study node similarity methods and shortest path algorithms
for career pathfinding. Finally, we leverage a term weighting method for
identifying which skills are most "distinctive" for different (types of)
occupations.
