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

2026-03-15

AI Summary

This work addresses the limitations of traditional keyword-based recruitment systems, which struggle with skill synonymy and nonlinear career trajectories, leading to inaccurate and opaque job matches. To overcome these challenges, the authors propose an intelligent job matching framework that integrates Transformer-based embeddings, a skill knowledge graph, and an explainable reranking mechanism. The approach enhances match quality through a multi-factor utility optimization that jointly considers skill compatibility, experience, location, salary, and company preferences. Innovatively combining knowledge graphs, semantic retrieval, and explainable AI, the method introduces an interpretable multi-criteria reranking strategy. The study also contributes the JobSearch-XS benchmark dataset. Experimental results demonstrate superior performance in skill generalization and retrieval tasks, accompanied by a deployable web interface, demonstration video, and installation package.

Technology Category

• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Learning Preferences or Rankings

Application Category

• 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
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes. Most systems act as keyword filters, failing to handle skill synonyms and nonlinear careers, resulting in missed candidates and opaque match scores. We introduce JobMatchAI, a production-ready system integrating Transformer embeddings, skill knowledge graphs, and interpretable reranking. Our system optimizes utility across skill fit, experience, location, salary, and company preferences, providing factor-wise explanations through resume-driven search workflows. We release JobSearch-XS benchmark and a hybrid retrieval stack combining BM25, knowledge graph and semantic components to evaluate skill generalization. We assess system performance on JobSearch-XS across retrieval tasks, provide a demo video, a hosted website and installable package.

Problem

• job matching
• skill synonyms
• nonlinear careers
• opaque match scores
• candidate filtering

Innovation

• Knowledge Graph
• Semantic Search
• Explainable AI
• Transformer Embeddings
• Job Matching