The rapid growth of Web usage for advertising job positions provides a great opportunity for real-time labour market monitoring. This is the aim of Labour Market Intelligence (LMI), a field that is becoming increasingly relevant to EU Labour Market policies design and evaluation. The analysis of Web job vacancies, indeed, represents a competitive advantage to labour market stakeholders with respect to classical survey-based analyses, as it allows for reducing the time-to-market of the analysis by moving towards a fact-based decision making model. In this paper, we present our approach for automatically classifying million Web job vacancies on a standard taxonomy of occupations. We show how this problem has been expressed in terms of text classification via machine learning. We also show how our approach has been applied to certain real-life projects and we discuss the benefits provided to end users.

Introduction

In the last few years, the diffusion of web-centric services is growing exponentially, and this allows a significant part of the European Labour demand to be communicated through specialised web portals and services. This has also led to the introduction of the term “Labour Market Intelligence” (LMI), which refers to the use and design of AI algorithms and frameworks for Labour Market Data to support decision-making. Motivating Example. In the on-line job market, a job vacancy is a document containing two main text fields: a title and a full description. The title shortly summarizes the job position, while the full description field usually includes the position details and the relevant skills the employee must possess. Table 1 shows two job vacancies extracted from specialised web sites. Though both advertisements seek for computer scientists, the differences in terms of job requirements, skills and corresponding educational levels are quite evident. The first job vacancy (A) is looking for a software developer, while the second one (B) is looking for a less qualified candidate, i.e., an ICT technician. Indeed, in the latter case, the only requested abilities are to be able to use certain solutions, and the knowledge of a programming language (optional) which is usually taught in some professional high-schools. Being able to catch these differences and to classify these two distinct occupational profiles promptly and using a standard taxonomy is mandatory for analysing, sharing, and comparing the web Labour market dynamics over different regions and countries, focusing on the skills they require and linking them to the ones expected for such positions. There is a growing interest in designing and implementing real LMI applications for Web Labour Market data that can support policy design and evaluation activities through evidence-based decision-making.