The field of explainable artificial intelligence (XAI) is gaining increasing importance in recent
years. As a consequence, several surveys have been published to explore the current state …

We propose a recommender system that, starting from a set of users' skills, identifies the
most suitable jobs as they emerge from a large dataset of Online Job Vacancies (OJVs). To …

The job market is evolving continuously due to changes in economic landscapes,
technological improvements, and skill requirements. In the era of digitalization, a wealth of …

Several studies have addressed the importance of context and users' knowledge and
experience in quantifying the usability and effectiveness of the explanations generated by …

Digitalization, automation, robotization and green transition are key current drivers changing
the labour markets and the structure of skills needed to perform tasks within jobs. Mitigating …

The need for explanations of ML systems is growing as new models outperform their
predecessors while becoming more complex and less comprehensible for their end-users …

The utilization of word embeddings—powerful models computed through Neural Network
architectures that encode words as vectors—has witnessed rapid growth across various …

Taxonomies are the mainstay of the semantic web as they aim at organising knowledge in
concepts linked by IS-A relationships. However, keeping such hierarchies updated and able …

Lexical taxonomies and distributional representations are largely used to support a wide
range of NLP applications, including semantic similarity measurements. Recently, several …

Lexical taxonomies are widely used to foster information retrieval and exchange in several
domains and applications. When there are multiple taxonomies, heterogeneity among them …