Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Ontology-guided job market demand analysis: A cross-sectional study for the data science field

: Sibarani, Elisa Margareth; Scerri, Simon; Morales, Camilo; Auer, Sören; Collarana, Diego


Association for Computing Machinery -ACM-:
Semantics 2017, 13th International Conference on Semantic Systems. Proceedings : Amsterdam, Netherlands, September 11 - 14, 2017
New York: ACM, 2017
ISBN: 978-1-4503-5296-3
International Conference on Semantic Systems (Semantics) <13, 2017, Amsterdam>
Conference Paper
Fraunhofer IAIS ()

The rapid changes in the job market, including a continuous year-on-year increase in new skills in sectors like information technology, has resulted in new challenges for job seekers and educators alike. The former feel less informed about which skills they should acquire to raise their competitiveness, whereas the latter are inadequately prepared to offer courses that meet the expectations by fast-evolving sectors like data science. In this paper, we describe efforts to obtain job demand data and employ a information extraction method guided by a purposely-designed vocabulary to identify skills requested by the job vacancies. The Ontology-based Information Extraction (OBIE) method employed relies on the Skills and Recruitment Ontology (SARO), which we developed to represent job postings in the context of skills and competencies needed to fill a job role. Skill demand by employers is then abstracted using co-word analysis based on a set of skill keywords and their co-occurrences in the job posts. This method reveals the technical skills in demand together with their structure for revealing significant linkages. In an evaluation, the performance of the OBIE method for automatic skill annotation is estimated (strict F-measure) at 79%, which is satisfactory given that human inter-annotator agreement was found to be automatic keyword indexing with an overall strict F-measure at 94%. In a secondary study, sample skill maps generated from the matrix of co-occurrences and correlation are presented and discussed as proof-of-concept, highlighting the potential of using the extracted OBIE data for more advanced analysis that we plan as future work, including time series analysis.