Abstract: The labor market is a system that is complex and difficult to manage. To overcome this challenge, the European Union has launched the ESCO project which is a language that aims to describe this labor market. In order to support the spread of this project, its dataset was presented as linked open data (LOD). Since LOD is usable and reusable, a set of conditions have to be met. First, LOD must be feasible and high quality. In addition, it must provide the user with the right answers, and it has to be built according to a clear and correct structure. This study investigates the LOD of ESCO, focusing on data quality and data structure. The former is evaluated through applying a set of SPARQL queries. This provides solutions to improve its quality via a set of rules built in first order logic. This process was conducted based on a new proposed ESCO ontology.

Adham Kahlawi, “An Ontology Driven ESCO LOD Quality Enhancement” International Journal of Advanced Computer Science and Applications(IJACSA), 11(3), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110308

@article{Kahlawi2020,
title = {An Ontology Driven ESCO LOD Quality Enhancement},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110308},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110308},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
author = {Adham Kahlawi}
}