The fast adoption of new technologies forces companies to continuously adapt their operations making it harder to predict workforce requirements. Several recent studies have attempted to predict the emergence of new roles and skills in the labour market from online job ads. This paper aims to present a novel ontology linking business transformation initiatives to occupations and an approach to automatically populating it by leveraging embeddings extracted from job ads and Wikipedia pages on business transformation and emerging technologies topics. To our knowledge, no previous research explicitly links business transformation initiatives, like the adoption of new technologies or the entry into new markets, to the roles needed. Our approach successfully matches occupations to transformation initiatives under ten different scenarios, five linked to technology adoption and five related to business. This framework presents an innovative approach to guide enterprises and educational institutions on the workforce requirements for specific business transformation initiatives.

翻译：新技术的快速普及迫使企业不断调整运营方式，这使得劳动力需求预测变得愈发困难。近年来，多项研究尝试通过在线招聘信息预测劳动力市场中的新兴岗位与技能。本文旨在提出一种将企业转型举措与职业相关联的创新本体，并介绍一种通过挖掘招聘广告以及关于企业转型与新兴技术主题的维基百科页面所提取的嵌入向量来自动填充该本体的方法。据我们所知，此前尚无研究明确将企业转型举措（如采用新技术或开拓新市场）与所需岗位相关联。我们的方法成功地在十种不同场景下将职业与转型举措相匹配——其中五种与技术应用相关，五种与商业领域相关。该框架为引导企业和教育机构针对特定企业转型举措规划劳动力需求提供了创新路径。