Skill Extraction Pipeline for Normalized Skill Graph Matching
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Solution Overview
Problem
Organizations face challenges in identifying suitable employees and matching skills across different contexts due to mismatches in skill names or descriptions, and skills are continuously evolving, requiring significant manual intervention.
Innovation Solution
A skill extraction pipeline system that utilizes machine learning models and generative AI to normalize and verify skill labels, generate new skill nodes, and create a skill graph for accurate skill matching, incorporating user feedback and telemetry metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to recognize and include new skills in skill matching programs, then skill matching accuracy can be maintained, but the time and effort required increases significantly
Solution Approach 1:
The system automatically discovers new skills by analyzing job descriptions and training program data, and self-updates the skill database without requiring manual intervention. The skill extraction pipeline autonomously normalizes skill names, identifies new skills, and integrates them into the matching system, enabling the system to serve and update itself.
Solution Approach 2:
The patent replaces manual mechanical processes of skill recognition and database updates with an automated computational system. Machine learning models and natural language processing algorithms substitute human analysts, automatically extracting skills from text data and updating the skill database through computational operations rather than manual effort.
2Adaptability or versatility
If different contexts or institutions use different skill names or skill descriptions, then each institution can maintain its own terminology, but skill matching between different contexts becomes difficult
Solution Approach 1:
The system introduces a normalization layer that acts as an intermediary between different institutional terminologies. This normalization process maps various skill names and descriptions to a standardized skill representation, enabling accurate matching across different contexts while preserving the original institutional terminology in the source data.
Solution Approach 2:
The skill database and matching system are designed to handle multiple skill naming conventions and descriptions simultaneously. The system universally processes different institutional terminologies through a unified framework, allowing it to adapt to various contexts while maintaining consistent matching functionality across all institutions.
Data Source
AI summary
Embodiments are directed to managing data for skill extraction pipeline. Skill spans may be determined based on skill information associated with job descriptions such that each skill span includes words included in the skill information. A comparison of the skill spans with other skill spans associated with a skill knowledge graph may be generated. Skill nodes may be generated for the unmatched skill spans. The new skill nodes may be integrated into the skill knowledge graph. Accordingly, the skill graph may be automatically updated for use in job/career applications including report generation.