HRG raph: Leveraging LLM s for HR Data Knowledge Graphs with Information Propagation-based Job Recommendation (2024)

Abstract: 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

## introduction knowledge graph (kg) is a semantic network that stores real-world entities and their relationships. it uses nodes representing objects, places, or persons, connected by edges defining relationships. it can integrate diverse data, contextualize information through linking and semantic metadata, and remain flexible, accommodating dynamic knowledge changes seamlessly @xcite @xcite . knowledge graphs can be highly effective for managing hr data, integrating diverse sources into a unified, structured representation @xcite . this is crucial for applications like recruitment and career path planning. by linking data with semantic metadata, kgs prevent misinterpretation, particularly in employee skill mapping and development. their flexibility allows easy adaptation to new data and requirements across various hr functions. kgs enhance recruitment precision, skill and career mapping, optimize recruitment processes, identify learning gaps, improve retention strategies, and facilitate organizational knowledge sharing. for employees, kgs offer better job searches and recommendations, providing strong support from their perspective @xcite . ## experiments with knowledge graphs we collect 200 cvs and 200 job descriptions from online job portals, ensuring the cvs had minimal this dataset includes 20 categories of jobs and cvs targeting these jobs. the categories are: information technology, business development, finance, advocate, accountant, engineering, chef, aviation, fitness, sales, banking, healthcare, consultant, construction, public relations, human resources, designer, arts, teacher, apparel. in cvs, there are 10 for each category, but in job descriptions, there are more jobs in it and engineering. prompts are provided in section b. while the core design remains the same, the prompts are slightly different for curriculum vitae and job description, each tuned to its specific modality. the full inference code with examples is available in the github repository. ## discussion we believe that transforming hr data into a knowledge graph holds a great promise in shaping the future of human resources data collection, management, and utilization. by envisioning hr data in this interconnected graph, organizations can unlock unprecedented insights, streamline recruitment processes, identify talent gaps, and foster employee growth. this approach not only enhances decisionmaking but also paves the way for a dynamic and adaptive hr ecosystem that propels organizational success in an ever-evolving landscape. ## conclusion this study introduces a framework leveraging llms and gnns to construct hr knowledge graphs from documents, working as a hr knowledge base for different hr tasks. the resulting kgs enhance various hr functions, including job matching, job area classification, and many more, demonstrating their efficacy through empirical evidence, benefiting both employers and employees.