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.
翻译:劳动力市场正在不断演变。职业正在变化、增加或消失,以适应当今市场的需求。近年来,由于全球化、数字化和从家庭转向工作等因素,这种变化的速度加快了。在选择就业时,不同的因素是相关的,例如文化适合性、报酬、提供自由程度等。要成功完成一个职业,所需(工作)与(求职者)拥有的技能之间的差距需要尽可能小。降低这种技能差距可以改善求职者与职业的适合性。在这份文件中,我们提出一个适合上述劳动力市场动态性质的定制技能与职业知识图表(KG),方法是利用现有技能和职业分类,用外部职位公布数据丰富。我们利用这个KG,并探索以技能为基础将工作与求职者匹配的若干应用。首先,我们研究将预测作为量化技能与职业相关性的手段,有助于优先考虑雇员的学习和发展。接下来,我们研究一种类似的方法和最短路径算法,用以“为职业选择最不同的选择方法” 。最后,我们利用这个KG,我们研究一种不同的选择方法,我们用最接近的方法和最短路径算法。