Marivate, Vukosi NMoorosi, Nyalleng2018-01-092018-01-092017-08Marivate, V.N. and Moorosi, N. 2017. Employment relations: A data driven analysis of job markets using online job boards and online professional networks. 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2017), 2nd International Workshop on Knowledge Management of Web Social Media (KMWSM 2017), 23-26 August 2017, Leipzig University, Leipzig, Germany978-1-4503-4951-2https://dl.acm.org/citation.cfm?id=3106426.3115589doi>10.1145/3106426.3115589http://hdl.handle.net/10204/9935Copyright: 2017 ACM. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website.Data from online job boards and online professional networks present an opportunity to understand job markets as well as how professionals transition from one job/career to another. We propose a data driven approach to begin to understand a slice of the South African job market. We do this by analysing data from career websites as well as a South African online professional networks. Our goals are to be able to group jobs given their descriptions, characterise career paths as well as to have some building blocks to be able to extract job position hierarchies given a description.enMachine learningGraph miningConference PresentationMarivate, V. N., & Moorosi, N. (2017). Employment relations: A data driven analysis of job markets using online job boards and online professional networks. ACM Digital Library. http://hdl.handle.net/10204/9935Marivate, Vukosi N, and Nyalleng Moorosi. "Employment relations: A data driven analysis of job markets using online job boards and online professional networks." (2017): http://hdl.handle.net/10204/9935Marivate VN, Moorosi N, Employment relations: A data driven analysis of job markets using online job boards and online professional networks; ACM Digital Library; 2017. http://hdl.handle.net/10204/9935 .TY - Conference Presentation AU - Marivate, Vukosi N AU - Moorosi, Nyalleng AB - Data from online job boards and online professional networks present an opportunity to understand job markets as well as how professionals transition from one job/career to another. We propose a data driven approach to begin to understand a slice of the South African job market. We do this by analysing data from career websites as well as a South African online professional networks. Our goals are to be able to group jobs given their descriptions, characterise career paths as well as to have some building blocks to be able to extract job position hierarchies given a description. DA - 2017-08 DB - ResearchSpace DP - CSIR KW - Machine learning KW - Graph mining LK - https://researchspace.csir.co.za PY - 2017 SM - 978-1-4503-4951-2 T1 - Employment relations: A data driven analysis of job markets using online job boards and online professional networks TI - Employment relations: A data driven analysis of job markets using online job boards and online professional networks UR - http://hdl.handle.net/10204/9935 ER -