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 scalability: The growth of online job postings makes manual extraction of relevant skills from job descriptions challenging.
manual skill extraction scalability: The growth 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 system uses natural language processing to identify and classify skills from unstructured text.
knowledge graph skill relationship representation: A knowledge graph represents relationships between extracted skills.
NLP skill identification and classification: An AI-based system uses natural language processing to identify and classify skills from unstructured text.
knowledge graph skill relationship representation: A knowledge graph represents relationships between extracted skills.
Results (2)
improved semantic understanding and decision-making:
improved semantic understanding and decision-making:
Research Domain
AI-driven job skill extraction and semantic analysis