JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI

Knowledge graphs improve matching beyond keyword matching: JobMatchAI uses skill knowledge graphs to handle synonyms and related skills that traditional keyword-based systems miss, addressing a key limitation where recruiters and job seekers fail to find relevant matches due to terminology differences.

Explainability is built into hiring decisions: The system provides "factor-wise explanations" showing how different criteria (skill fit, experience, location, salary, company preferences) contribute to match scores, making hiring recommendations transparent rather than opaque "black box" decisions.

Non-linear career paths are now matchable: Unlike most systems that fail with unconventional career trajectories, JobMatchAI can identify qualified candidates whose experience doesn't follow traditional linear progressions, potentially reducing bias against career changers and diverse backgrounds.

Production-ready responsible AI requires multiple components: The system combines Transformer embeddings, knowledge graphs, semantic search, and interpretable reranking—demonstrating that responsible AI in hiring requires integrating multiple technologies rather than relying on a single approach.

Benchmark transparency enables accountability: The authors released JobSearch-XS benchmark and made their system available as both a hosted website and installable package, supporting reproducibility and external evaluation of hiring AI systems' fairness and performance.