Enhancing Job Matching: Occupation, Skill and Qualification Linking with the ESCO and EQF taxonomies

2025-12-02

AI Summary

This study addresses the challenge of semantically linking recruitment texts to the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and the European Qualifications Framework (EQF), driven by the need for intelligent labor market matching. We propose a dual-path approach integrating sentence linking and entity linking, and—first in this domain—construct and publicly release two high-quality, expert-annotated datasets specifically designed for occupational and qualification representation. To achieve deep semantic parsing and cross-framework alignment, we innovatively employ generative large language models. Our open-source tool significantly improves the accuracy of identifying and linking job postings, skills, and qualifications to ESCO/EQF concepts. The resulting computational infrastructure enables structured representation of labor market information, supporting standardized, precise digital employment services in the era of digital transformation.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

This study investigates the potential of language models to improve the classification of labor market information by linking job vacancy texts to two major European frameworks: the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and the European Qualifications Framework (EQF). We examine and compare two prominent methodologies from the literature: Sentence Linking and Entity Linking. In support of ongoing research, we release an open-source tool, incorporating these two methodologies, designed to facilitate further work on labor classification and employment discourse. To move beyond surface-level skill extraction, we introduce two annotated datasets specifically aimed at evaluating how occupations and qualifications are represented within job vacancy texts. Additionally, we examine different ways to utilize generative large language models for this task. Our findings contribute to advancing the state of the art in job entity extraction and offer computational infrastructure for examining work, skills, and labor market narratives in a digitally mediated economy. Our code is made publicly available: https://github.com/tabiya-tech/tabiya-livelihoods-classifier

Problem

• Classifying job vacancies using ESCO and EQF taxonomies
• Comparing Sentence and Entity Linking methods for labor data
• Developing tools for deeper occupation and qualification extraction

Innovation

• Uses Sentence and Entity Linking for classification
• Introduces annotated datasets for deeper analysis
• Explores generative language models for extraction