Ontology-quality Evaluation Methodology for Enhancing Semantic Searches and Recommendations: A Case Study

Paula Peña, Raquel Trillo-Lado, Rafael Hoyo, María Rodríguez-Hernández, David Abadía


In the big data era, there exist an increasing demand of models and tools to evaluate quality of data used in decision-making and search processes, as decision based on wrong and poor data quality can lead to enormous loss. Thus, data has become an asset and the most powerful enabler of any organization. In this context, ontologies and semantic techniques have gained importance in order to represent data sources and metadata during the last decades. In this paper, we describe our work-in-progress concerning to the generation of models that encourage data quality through the use of ontologies. In particular, we present a use case where an enriched ontological model of ESCO (European Skills, Competences, Qualifications and Occupations) is used to improve the effectiveness of a search and recommendation system. In more detail, we focus on how ESCO is enriched by following METHONTOLOGY methodology and 101 methodological guidelines. We also provide the design of a search and recommendation system oriented to labour market that exploits the enhanced ontology to suggest qualifications required by job seekers and employees to reach a specific occupation position and different training itineraries to get those recommended qualifications.


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