Ontology-Based Platform for Mapping Skills to Job Titles
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Solution Overview
Problem
Conventional approaches to matching job-related skills with job titles are hindered by inconsistent terminology, outdated manual skill reporting, and a lack of automatic skill context mapping, leading to inaccurate skill assessments and inefficient job searching.
Innovation Solution
An ontology-based technology platform that preprocesses and cleans job titles and skills data, using machine learning and natural language processing to create a unified ontology, automatically maps skills to job titles, and integrates user aspirations, enabling precise skill matching and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual skill reporting is used, then skill profiles can be created, but the skill profiles become outdated and inaccurate over time
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and preprocessing skill data, job postings, and professional profiles in advance. Machine learning models are trained beforehand to automatically match skills with job titles, so when skill profiles need updating, the matching is already prepared, eliminating delays.
Solution Approach 2:
The skill profile system serves itself by automatically detecting skill changes from job postings and professional activities, then self-updating profiles without manual intervention. The system monitors its own data sources and autonomously maintains accuracy, preventing skill profiles from becoming outdated.
2Measurement precision
If conventional skill matching approaches are used, then job searching can be performed, but the matching accuracy is low due to inconsistent terminology
Solution Approach 1:
The patent introduces an intermediary ontology layer that standardizes terminology between diverse skill sources and job postings. This ontology acts as a mediator, mapping various terminologies to a common framework, thereby improving matching accuracy without requiring complete standardization of all source materials.
Solution Approach 2:
The system dynamically adjusts matching parameters and thresholds based on the specific context of each skill-job pairing. Rather than using fixed matching criteria, the system modifies parameters like similarity weights and confidence thresholds to optimize accuracy for different terminology patterns and domains.
3Loss of information
If automatic skill mapping is implemented, then skill context can be preserved, but the system complexity increases
Solution Approach 1:
The patent segments the ontology processing into distinct modular components: skill extraction, context analysis, relationship mapping, and profile integration. Each module handles a specific aspect of the mapping process independently, reducing overall system complexity while preserving skill context through structured information flow between segments.
Data Source
AI summary
Systems and methods are provided for generating and using an ontology-based technology platform and job titles data structure for mapping skills with job titles.