Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Mayank Vyaas, Abhijit Chakrabroty, Vivek Gupta

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.

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

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.
Paper Structure (40 sections, 3 equations, 3 figures, 7 tables)

This paper contains 40 sections, 3 equations, 3 figures, 7 tables.

Table of Contents

  1. Introduction
  2. System Overview
  3. Ingestion and Semantic Processing
  4. Knowledge Graph Design
  5. Hybrid Search Pipeline
  6. Stage 1: Query Enrichment.
  7. Stage 2: Parallel Multi-Source Retrieval.
  8. Stage 3: Query-Adaptive Reciprocal Rank Fusion.
  9. Stage 4: Hard-Constraint Filtering.
  10. Stage 5: Multi-Factor Reranking.
  11. Explainable Reranking
  12. System Demonstration
  13. Smart Search (Resume-Driven).
  14. Keyword Search with Semantic Enrichment.
  15. Evaluation
  16. ...and 25 more sections

Figures (3)

  • Figure 1: End-to-end JobMatchAI hybrid search pipeline architecture.
  • Figure 2: JobHunt-AI Platform Interface
  • Figure 3: Visualization of the JobMatchAI knowledge graph illustrating the relationships between candidates, jobs, skills, locations, and companies used for hybrid retrieval and explainable reranking.