The rapid changes on the job market and the dramatic usage of the Web have triggered the need to analyze online job adverts. This paper presents an quantitative method to infer employers skill demand using co-word analysis based on skills keyword. These keywords are extracted automatically by an Ontology-based Information Extraction (OBIE) method. An ontology called Skills and Recruitment Ontology (SARO) has been developed to represent job postings in the context of skills and competencies needed to fill a job role. During the extraction and annotation of keywords, we focus on job posting attributes and job specific skills (Tool, Product, Topic). We present our system where cross-sectional study is decoupled in two phases: (1) a customized-pipeline for extracting information whose results are a matrix of co-occurrences and correlation; and (2) content analysis to visualize the keywords' structure and network. This method reveals the technical skills in demand together with their structure for revealing significant linkages. The evaluation of OBIE method indicates the promising result of automatic keyword indexing with an overall strict F-measure at 79%. The advantage of using an ontology and reusing semantic categories enables other research groups to reproduce this method and its results.
Director
Leibniz Information Center for Science & Technology
https://www.tib.eu/en/
Following stations at the universities of Dresden, Ekaterinburg, Leipzig, Pennsylvania, Bonn and the Fraunhofer Society, Prof. Auer was appointed Professor of Data Science and Digital Libraries at Leibniz Universität Hannover and Director of the TIB in 2017.
