AI-Driven Job Skill Extraction and Semantic Analysis Using Natural Language Processing and Knowledge Graph
Ananya Das Das
Zenodo (CERN European Organization for Nuclear Research)
Problems Identified (2)
Manual Skill Extraction Challenge: The growing volume of online job postings makes manual extraction of relevant skills from job descriptions challenging.
Manual Skill Extraction Challenge: The growing volume of online job postings makes manual extraction of relevant skills from job descriptions challenging.
Proposed Solutions (4)
NLP Skill Identification and Classification: An AI-based NLP system identifies and classifies skills from unstructured text.
Knowledge Graph Skill Relationship Modeling: A knowledge graph represents relationships between extracted skills.
NLP Skill Identification and Classification: An AI-based NLP system identifies and classifies skills from unstructured text.
Knowledge Graph Skill Relationship Modeling: A knowledge graph represents relationships between extracted skills.
Results (2)
Improved Semantic Understanding and Decision-Making:
Improved Semantic Understanding and Decision-Making:
Research Domain
NLP for job skill extraction and semantic analysis