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E Dar, J. Dorn:
"Ontology Based Classification System for Online Job offers";
Vortrag: International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan; 03.04.2018 - 04.04.2018; in: "Proceedings of the International Conference on Computing, Mathematics and Engineering Technologies", IEEE Explore, (2018).

The significance of employment in a setup of society is quite evident. Publishing of jobs online opened the opportu- nities for researchers to find an automated method to retrieve job offers. To automate the categorization of job opportunities we need a classification model either from Machine Learning or some other method. In this paper, we devised an ontology- based classifier which is used to classify job offers. More than 5000 job offers were collected from multiple existing job offer websites. We used an ontology to, extraction concepts from job offers text description, find the minimum threshold for classification, and developed a classification model based on this ontology. We did not use any Machine Learning algorithm to develop this classifier. We evaluated this classifier according to Machine learning evaluation model, training, and testing dataset. Our classifier showed >90% accuracy, precision, and recall for both training and testing dataset. Our finding paves the way to automate the job offers categorization and retrieval.

Job Offer, Ontology, Taxonomy, RMSE Curve, IT job offer, Text Classification