# HRGraph: Leveraging LLMs for HR Data Knowledge Graphs with Information Propagation-based Job Recommendation

arXiv: https://arxiv.org/abs/2408.13521 · HTML: https://mlsanity.com/paper/2408.13521/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [Knowledge Graphs on the Web -- an Overview](https://mlsanity.com/paper/2003.00719/) (2020)
- [Deep Learning on Knowledge Graph for Recommender System: A Survey](https://mlsanity.com/paper/2004.00387/) (2020)
- [Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text](https://mlsanity.com/paper/2305.08804/) (2023)
- [Assessing the Reliability of Large Language Model Knowledge](https://mlsanity.com/paper/2310.09820/) (2023)
- [BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering](https://mlsanity.com/paper/2404.03528/) (2024)

## Built on by (most cited first, 3 of 3)

- [Large Language Model Enhanced Recommender Systems: A Survey](https://mlsanity.com/paper/2412.13432/) (2024)
- [Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks](https://mlsanity.com/paper/2411.08550/) (2024)
- [Multi-View Graph Convolution Network for Internal Talent Recommendation Based on Enterprise Emails](https://mlsanity.com/paper/2508.20328/) (2025)

## Abstract (arXiv)

Knowledge Graphs (KGs) serving as semantic networks, prove highly effective in managing complex interconnected data in different domains, by offering a unified, contextualized, and structured representation with flexibility that allows for easy adaptation to evolving knowledge. Processing complex Human Resources (HR) data, KGs can help in different HR functions like recruitment, job matching, identifying learning gaps, and enhancing employee retention. Despite their potential, limited efforts have been made to implement practical HR knowledge graphs. This study addresses this gap by presenting a framework for effectively developing HR knowledge graphs from documents using Large Language Models. The resulting KG can be used for a variety of downstream tasks, including job matching, identifying employee skill gaps, and many more. In this work, we showcase instances where HR KGs prove instrumental in precise job matching, yielding advantages for both employers and employees. Empirical evidence from experiments with information propagation in KGs and Graph Neural Nets, along with case studies underscores the effectiveness of KGs in tasks such as job and employee recommendations and job area classification. Code and data are available at : https://github.com/azminewasi/HRGraph