{"title":"DataOps for Societal Intelligence: a Data Pipeline for Labor Market Skills Extraction and Matching","authors":"D. Tamburri, W. Heuvel, Martin Garriga","doi":"10.1109/IRI49571.2020.00063","DOIUrl":null,"url":null,"abstract":"Big Data analytics supported by AI algorithms enable skills localization and retrieval, in the context of a labor market intelligence problem. We formulate and solve this problem through specific DataOps models, blending data sources from administrative and technical partners in several countries into cooperation, creating shared knowledge to support policy and decision-making. We then focus on the critical task of skills extraction from resumes and vacancies featuring state-of-the-art machine learning models. We showcase preliminary results with applied machine learning on real data from the employment agencies of the Netherlands and the Flemish region in Belgium. The final goal is to match these skills to standard ontologies of skills, jobs and occupations.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"89 1","pages":"391-394"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI49571.2020.00063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16

Abstract

Big Data analytics supported by AI algorithms enable skills localization and retrieval, in the context of a labor market intelligence problem. We formulate and solve this problem through specific DataOps models, blending data sources from administrative and technical partners in several countries into cooperation, creating shared knowledge to support policy and decision-making. We then focus on the critical task of skills extraction from resumes and vacancies featuring state-of-the-art machine learning models. We showcase preliminary results with applied machine learning on real data from the employment agencies of the Netherlands and the Flemish region in Belgium. The final goal is to match these skills to standard ontologies of skills, jobs and occupations.
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社会智能的数据操作:劳动力市场技能提取和匹配的数据管道
在劳动力市场情报问题的背景下,人工智能算法支持的大数据分析可以实现技能定位和检索。我们通过特定的DataOps模型制定和解决这一问题,将来自多个国家的行政和技术合作伙伴的数据源融合到合作中,创建共享知识以支持政策和决策。然后,我们将重点放在从简历和职位空缺中提取技能的关键任务上,并采用最先进的机器学习模型。我们展示了应用机器学习对来自荷兰和比利时佛兰德地区的就业机构的真实数据的初步结果。最终目标是将这些技能与技能、工作和职业的标准本体相匹配。
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