Multilingual Skill Extraction for Job Vacancy–Job Seeker Matching in Knowledge Graphs (2025)

Abstract: In the modern labor market, accurate matching of job vacancies with suitable candidate CVs is critical. We present a novel multilingual knowledge graph-based framework designed to enhance the matching by accurately extracting the skills requested by a job and provided by a job seeker in a multilingual setting and aligning them via the standardized skill labels of the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy. The proposed framework employs a combination of state-of-the-art techniques to extract relevant skills from job postings and candidate experiences. These extracted skills are then filtered and mapped to the ESCO taxonomy and integrated into a multilingual knowledge graph that incorporates hierarchical relationships and cross-linguistic variations through embeddings. Our experiments demonstrate a significant improvement of the matching quality compared to the state of the art.

## introduction in the modern labour market, job portals serve as key intermediaries, connecting employers with potential employees by comparing job postings with the cvs of job seekers, to find the best possible matches and liaise both parties. 1 matching of the required skills in a job posting with the skills outlined in the candidate's experience is of special relevance. for implementation of this matching, recently a number of ai-based techniques have been proposed. zero-shot recommendation models @xcite , attention-based scoring mechanisms @xcite , embedding-based models @xcite , or tensor decomposition @xcite have been used, with an emphasis of surface-level features, such as geographic location and user data, including click-through rates and other user engagement metrics, without a deeper analysis of the semantics of the listed skills. other, more promising, proposals draw upon the skills taxonomy of the european skills, competences, qualifications and occupations (esco) classification foot_1 , which is, together with its us equivalent o*net foot_2 , a primary instrument used in international job markets for job offer and candidate experience annotation. for instance, @xcite identify esco skill labels in job offers / candidate experiences and provide the reasoning, which leads to the identification of skills by the use of a large language model (llm), giving guidance to it by detailed prompts, accompanied by manually created examples, but without that the llm can re-evaluate its reasoning chain. this is different from @xcite , which uses the "chain of thought" (cot) strategy to make the llm use its own reasoning explanations as incontext examples. however, as other proposals in the field, these proposals neglect the hierarchically organized, contextually diverse information in the esco classification.furthermore, they are prone to biases inherent in llms when processing textual data, as they do not involve any human expert control or human-generated ground truth data. to address these challenges and to improve the quality of skill extraction from job postings and candidate experiences and their matching, we developed a knowledge graph-based multi-step framework. the proposed architecture foresees skill extraction using several state-of-the-art skill selection skills @xcite @xcite to whose output @xcite 's cot prompting technique is applied to select the most relevant skills by comparing the extracted skills with the esco skill names and their corresponding descriptions. human labelers provide ground truth data, based on which the model is optimized. the finally selected skills from the job postings and candidate experiences are linked in a knowledge graph with the esco skills. the graph is fine-tuned with multilingual (in our case, spanish) embeddings for effective matching in a multilingual context. our tests demonstrate that the developed framework significantly outperforms the state-of-the-art skill extraction and job posting -candidate experience matching. the framework is about to be deployed in a leading european job portal. it will serve as a complementary instrument for human job recruiters rather than a fully automated service, in order to minimize any risk of faulty recruitment suggestions. ## related work as already mentioned, a number of works deal with the identification and matching of skills in job postings and candidate experiences @xcite . for instance, @xcite and @xcite focus on skill identification through span labeling and binary classification. @xcite use sequence tagging with lstm neural networks, and @xcite employ exact match for skill spans. both require extensive optimization and are often limited by their dependency on specific languages and datasets. @xcite utilizes exact string matching with nlp techniques for label extraction and incorporates a multi-hop job-skill graph neural network with a graph isomorphism network encoder, employing one-vs-many classification with softmax attention and weighting skills based on their frequency in job postings. @xcite emphasize the generation of mention-entity pairs using esco skills as ground truth labels, their methodology, which heavily depends on the precise detection of mentions and the disambiguation of contexts, proves insufficient in real-world applications where such extraction methods are not readily available. @xcite demonstrates how knowledge graphs can bridge education and employment domains to support job seekers' skill development. they developed jobedkg, an uncertain knowledge graph embeddings to model relationships between job market and educational entities to predict required skills based on career choices. the systems by @xcite approach job-resume matching primarily as a recommender system, incorporating non-textual inputs like geo-location and evaluating performance based on click-through rates. this methodology inherently restricts the comparison to surface-level features shared between candidate experiences and job postings, neglecting the deeper semantic relationships and context that are crucial for accurate skill matching. similarly, @xcite experiment with zero-shot learning using various embedding models and dimensions. while this approach may be effective for exploring a broad understanding of industrial problems, it overlooks several recent findings. relying solely on embeddings with zero-shot classification has notable weaknesses, such as the lack of mechanisms to mitigate residual biases in textual data and limited capability to integrate diverse data sources. moreover, zero-shot classification models have been shown to be outperformed by in-context learning models, particularly llm, which offer superior contextual understanding and adaptability @xcite . apart from those that we already cited, in particular, @xcite needs to be highlighted. @xcite introduces "in-context learning for extreme multi-label classification" and built on dspy library @xcite . it consists of a collection of dspy programs that enable stateful interactions with llm. the llm integrates the cot methodology with retrieval-augmented generation for skill extraction, where classes-specifically, esco skill names-are provided as embeddings. the model learns classification through a novel optimization technique that fine-tunes prompts at each step and generates synthetic examples to enhance in-context learning (icl) @xcite , thereby aiming to maximize classification metrics. we use @xcite as foundation. we employ human-labellers to evaluate the skills extracted by this model and introduce enhancements to improve contextual understanding of the llm beyond the conventional nlp techniques utilized in @xcite ) additionally, we adapt this model for multilingual applications and incorporate the hierarchical skill relationships defined in the esco taxonomy. ## the framework in what follows, we first provide an overview of our approach as a whole and then discuss the skills extraction, filtering of the extracted skills and their subsequent mapping onto the esco skills taxonomy, and construction of the knowledge graph as a way of matching the skills extracted from the job postings and candidate experiences. ## experiments 4.1 technical infrastructure to ensure privacy of companies and users, all models in our experiments were executed locally 152 within an isolated environment. foot_9 blackbox models with internet access, which could potentially share sensitive data were restricted to processing only anonymized job postings and were never applied to candidate experiences. as technical infrastructure, we use the dspy library @xcite , which has been designed to create and optimize language model (lm) pipelines. we employed several dspy programs to structure the data and build our experimental pipeline. one dspy program processed chunks of cv data to generate synthetic job experiences that ideally match job postings as described in 'data' section. another dspy program, namely skill selection model, see figure 2 , was used to automatically normalize assigned skills against the esco taxonomy. this ensured that no extracted skill was omitted. given the variability in user-generated job titles, another dspy program was employed to generate unique job titles, thereby maintaining the integrity and uniqueness of the knowledge graph. the knowledge graph is constructed using the stellargraph library (data61, 2018). data is stored in amazon s3 buckets, with computational tasks executed on an rtx 3090ti gpu for the local llm and deep learning models. additionally, the framework utilizes the claude api via amazon bedrock. the entire framework is containerized and fully prepared for testing prior to deployment. ## conclusions our knowledge graph-based framework for candidate experience-job posting matching combines extensions of several state-of-the-art techniques, which leads to a versatile and robust application, apt for industrial use. the integration of entity linking mechanisms @xcite enhances contextual precision, while extreme multi-label classification @xcite addresses the extensive taxonomy of potential competencies, and the use llm with few-shot in-context learning methodologies @xcite ensures a high robustness of the skill extraction task. our extension of @xcite 's model provides enhanced reasoning capability by utilizing a cot technique that allows llm to review its own reasoning. it also enforces the model to respond in the input language, even when provided with context in another language. furthermore, it normalizes the selected skills against the esco taxonomy and incorporates a hint at an intermediate reasoning step, guiding the model to refine its initial output. the extended model significantly improves on the ability to accurately process multilingual data, compared to previous methods that relied on string matching or regular expressions to map extracted skills to the esco taxonomy. finally, representing job postings, candidate experiences and skills as nodes within a knowledge graph allows for detailed and structured analysis of skill overlap and compatibility, often missed in linear or non-relational models @xcite .