Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs

2025-09-11

AI Summary

To address inaccurate and poorly interpretable job-title matching in resume recommendation systems—caused by low lexical overlap or semantic ambiguity—this paper proposes a hierarchical matching method integrating semantic modeling with domain knowledge. Methodologically: (1) it introduces a self-supervised hybrid architecture coupling fine-tuned SBERT with a graph neural network, explicitly injecting domain knowledge graphs into semantic matching; (2) it designs a hierarchical evaluation strategy that performs fine-grained analysis across semantic relevance intervals, revealing model-behavior discrepancies obscured by global metrics. Experiments show a 25% reduction in RMSE over strong baselines on the high-relevance subset, significantly improving both matching accuracy and decision interpretability. The core contribution is a knowledge-enhanced hierarchical semantic alignment framework that jointly improves matching robustness and explainability.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Learning Preferences or Rankings

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Semantic Textual Relatedness (STR) captures nuanced relationships between texts that extend beyond superficial lexical similarity. In this study, we investigate STR in the context of job title matching - a key challenge in resume recommendation systems, where overlapping terms are often limited or misleading. We introduce a self-supervised hybrid architecture that combines dense sentence embeddings with domain-specific Knowledge Graphs (KGs) to improve both semantic alignment and explainability. Unlike previous work that evaluated models on aggregate performance, our approach emphasizes data stratification by partitioning the STR score continuum into distinct regions: low, medium, and high semantic relatedness. This stratified evaluation enables a fine-grained analysis of model performance across semantically meaningful subspaces. We evaluate several embedding models, both with and without KG integration via graph neural networks. The results show that fine-tuned SBERT models augmented with KGs produce consistent improvements in the high-STR region, where the RMSE is reduced by 25% over strong baselines. Our findings highlight not only the benefits of combining KGs with text embeddings, but also the importance of regional performance analysis in understanding model behavior. This granular approach reveals strengths and weaknesses hidden by global metrics, and supports more targeted model selection for use in Human Resources (HR) systems and applications where fairness, explainability, and contextual matching are essential.

Problem

• Improving job title matching using semantic textual relatedness
• Enhancing semantic alignment and explainability with knowledge graphs
• Addressing limited lexical overlap in resume recommendation systems

Innovation

• Self-supervised hybrid architecture combining embeddings and knowledge graphs
• Stratified evaluation partitioning semantic relatedness continuum
• Graph neural networks integrating knowledge graphs with text embeddings