  In the last few decades, companies are interested to adopt an online
automated recruitment process in an international recruitment environment. The
problem is that the recruitment of employees through the manual procedure is a
time and money consuming process. As a result, processing a significant number
of applications through conventional methods can lead to the recruitment of
clumsy individuals. Different JD/Resume matching model architectures have been
proposed and reveal a high accuracy level in selecting relevant candidatesfor
the required job positions. However, the development of an automatic
recruitment system is still one of the main challenges. The reason is that the
development of a fully automated recruitment system is a difficult task and
poses different challenges. For example, providing a detailed matching
explanation for the targeted stakeholders is needed to ensure a transparent
recommendation. There are several knowledge bases that represent skills and
competencies (e.g, ESCO, O*NET) that are used to identify the candidate and the
required job skills for a matching purpose. Besides, modernpre-trained language
models are fine-tuned for this context such as identifying lines where a
specific feature was introduced. Typically, pre-trained language models use
transfer-based machine learning models to be fine-tuned for a specific field.
In this proposal, our aim is to explore how modern language models (based on
transformers) can be combined with knowledge bases and ontologies to enhance
the JD/Resume matching process. Our system aims at using knowledge bases and
features to support the explainability of the JD/Resume matching. Finally,
given that multiple software components, datasets, ontology, andmachine
learning models will be explored, we aim at proposing a fair, ex-plainable, and
traceable architecture for a Resume/JD matching purpose.
