Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs
2025-09-12
Summary
The article discusses a study on improving job title matching using Semantic Textual Relatedness (STR) and Knowledge Graphs (KGs) to enhance resume recommendation systems. By integrating fine-tuned Sentence-BERT models with domain-specific knowledge graphs, the study aims to improve semantic alignment and explainability, offering more nuanced and contextually relevant job matches. The approach involves partitioning STR scores into regions (low, medium, high) for better performance analysis and model behavior understanding.
Why This Matters
In Human Resources, accurate and explainable job matching is crucial for fair and effective hiring decisions. Traditional models often lack transparency, which can undermine user trust and regulatory compliance. This study highlights the potential of combining STR with KGs, not only to enhance matching accuracy but also to provide insights into the reasoning behind recommendations, which is essential for building trust and ensuring fairness in HR systems.
How You Can Use This Info
Professionals in HR and recruitment can leverage the insights from this study to enhance their job recommendation systems, making them more transparent and trustworthy. By understanding the importance of explainability and the integration of KGs, organizations can improve their hiring processes, ensuring that recommendations are not only accurate but also justifiable. This approach can also be adapted to other domains requiring semantic matching, such as academic or legal fields.