SBIR Phase I: A Big Data Skills-To-Tasks Ontology For Career Mapping, Job Matching, And Talent Acquisition.

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是使人才搜索更快、更有效。更具体地说，它的目标是更容易将公开职位与求职者的才华联系起来。这里提出的创新将在劳动力市场发生重大变化的时候重新定义求职，该项目开发的技术将使求职远远超越一页简历，更多地关注成功工作所需的技能。这样，这项创新将使求职者能够找到与他们的职业兴趣相一致的新机会，并使雇主能够找到与他们的项目需求相匹配的熟练人才。使用这项技术的雇主将能够根据他们需要完成的工作来搜索人才，而不一定知道任务将如何完成。同样，求职者将能够找到他们可能不知道存在但仍与他们的职业目标高度一致的工作。这个小型企业创新研究(SBIR)第一阶段项目将开发一个技能与任务匹配系统，作为使用工人技能来执行工作中哪些任务的基础。目前，有关职业技能内容的现有数据不能反映行业、地理或时间上的差异。它也不包括员工当前技能对新工作场所任务的适用性，这是提高技能和再培训计划的一个重要考虑因素。这一限制使得21世纪就业市场中信息不对称的一个主要来源完好无损，在这个市场上，最有效的人才搜索策略仍然是同行推荐。同样，现有数据缺乏粒度，限制了政策制定者衡量技能差距、确定职业道路和设计当地经济最需要的培训计划的有效性。该项目中提出的研究将编制劳动力市场中正在演变的技能和工作活动的最新概要，并绘制它们之间的关系图。这项技术将提供对劳动力市场的动态和细粒度的看法，并提高可供工业、教育、劳动力发展以及未来工作研究的决策者使用的信息的质量。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to make talent search faster and much more effective. More specifically, it will aim to make it easier to connect open jobs with the talents of job seekers. The innovation proposed here will redefine job search in a time when the labor market is in major flux, and the technology developed in this project will enable job searching to move far beyond a one-page resume and focus more on the skills needed to succeed in a job. In so doing, the innovation will enable job seekers to find new opportunities that are aligned with their career interests and enable employers to find skilled talent matched to their project needs. Employers using this technology will be able to search for talent according to what they need performed, without necessarily knowing how the task will be performed. Similarly, job seekers will be able to search for jobs that they may not have known existed but are still strongly aligned with their career goals.This Small Business Innovation Research (SBIR) Phase I project will develop a skills-to-tasks matching system that serves as a basis for which worker skills are employed to perform which tasks on the job. Currently, available data on the skill content of occupations does not capture variation across industries, geographies, or over time. Nor does it capture the applicability of a worker's current skills to new workplace tasks, an important consideration for up-skilling and re-training programs. This limitation has left a major source of information asymmetry intact in the 21st century job market, where the most effective talent search strategy remains peer-referrals. This same lack of granularity in available data has limited the effectiveness of policymakers to measure the skills-gap, identify career pathways, and design training programs most needed in their local economies. The research proposed in this project will produce an up-to-date compendium of the skills and work activities that are evolving in the labor market and map the relationships between them. This technology will provide a dynamic and granular view of the labor market and improve the quality of information available to decision-makers in industry, education, workforce development, as well as for the study of the future of work.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.