# Job Posting-Enriched Knowledge Graph for Skills-based Matching

arXiv: https://arxiv.org/abs/2109.02554 · HTML: https://mlsanity.com/paper/2109.02554/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2021.

## Built on by (most cited first, 8 of 8)

- [MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations](https://mlsanity.com/paper/2410.08319/) (2024)
- [Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks](https://mlsanity.com/paper/2311.06383/) (2023)
- [Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules](https://mlsanity.com/paper/2308.16770/) (2023)
- [Occupation similarity through bipartite graphs](https://mlsanity.com/paper/2202.11064/) (2022)
- [NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching](https://mlsanity.com/paper/2506.19058/) (2025)
- [Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics](https://mlsanity.com/paper/2509.04942/) (2025)
- [Graph-Based Analysis of AI-Driven Labor Market Transitions: Evidence from 10,000 Egyptian Jobs and Policy Implications](https://mlsanity.com/paper/2601.06129/) (2026)
- [When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction](https://mlsanity.com/paper/2610.20186/) (2026)

## Abstract (arXiv)

The labor market is constantly evolving. Occupations are changing, being added, or disappearing to fit the needs of today's market. In recent years the pace of this change has accelerated, due to factors such as globalization, digitization, and the shift to working from home. Different factors are relevant when selecting employment, e.g., cultural fit, compensation, provided degree of freedom. To successfully fulfill an occupation the gap between required (by the job) and possessed (by the job seeker) skills needs to be as small as possible. Decreasing this skill-gap improves the fit between a job candidate and occupation. In this paper we propose a custom-built Skills & Occupation Knowledge Graph (KG) that fits the above described dynamic nature of the labor market, by leveraging existing skills and occupation taxonomies enriched with external job posting data. We leverage this KG and explore several applications for skills-based matching of jobs to job seekers. First, we study link prediction as a means to quantify relevance of skills to occupations, which can help in prioritizing learning and development of employees. Next, we study node similarity methods and shortest path algorithms for career pathfinding. Finally, we leverage a term weighting method for identifying which skills are most "distinctive" for different (types of) occupations.