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.
Economists and social scientists are increasingly making use of web data to address socio-economic issues and to integrate existing sources of information. The data produced by online platforms and websites could produce a lot of useful and multidimensional information with a variety of potential applications in socio-economic analysis. In this respect, with the internet growth and knowledge, many aspects of job search have been transformed due to the availability of online tools for job searching, candidate searching and job matching. In European countries there is growing interest in designing and implementing real labour market information system applications for internet labour market data in order to support policy design and evaluation through evidence-based decision-making. The analysis of labour market web data could provide useful information for policy-makers to define labour market strategies as big data, jointly with official statistics, support policy makers in a pressing policy question namely “How to tackle the mismatch between jobs and skills?”. In this regard, the topic of skills gap, how to measure it and how to bridge it with education and continuous training have been tackled by using the big data collection, such as the Cedefop (European Center for the Development of Vocational Training) initiative and the Wollybi Project (made by Burning Glass). In this framework, this contribution focuses on the issues arising from the use (and the usefulness) of on-line job vacancy data to analyse the Italian labour market by using the Wollybi data available for the years 2019 and 2020. Furthermore, the availability of data for the year 2020, will allow us to evaluate whether there has been an impact of COVID19 in terms of needed skills and required occupations in the online job vacancies.
Improving NLP outputs by extracting structured data from unstructured data is crucial, and several tools are available for the English language to achieve this objective. However, little attention has been paid to the Arabic language. This research aims to address this issue by enhancing the quality of DBpedia data. One limitation of DBpedia is that each resource can belong to multiple types and may not represent the intended concept. Additionally, some resources may be assigned incorrect types. To overcome these limitations, this study proposes creating a new ontology to represent Arabic data using the DBpedia ontology, followed by an algorithm to verify type assignments using the resource's title metadata and similarity between resources' descriptions. Finally, the research builds an entity annotation tool for Arabic using the verified dataset.
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