Hyperplane Optimization for Skill Ontology Precision
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Solution Overview
Problem
Current job matching tools face challenges in accurately identifying required skills and creating effective learning plans due to unstructured data and overly specific or general keywords, leading to inefficient candidate selection and skill development.
Innovation Solution
A system that generates a high-precision description of target skills using domain-specific language by ingesting data sets, semantically analyzing them to create a skill ontology, and applying hyperplane optimization to separate priority skills, enabling the creation of customized job descriptions and learning plans tailored to specific organizational needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based job matching tools are used, then the process is simple and fast, but the precision in identifying required skills is low
Solution Approach 1:
The patent introduces an intermediary layer between raw job descriptions and skill matching: a skill ontology generated through semantic analysis. This intermediary structure organizes skills hierarchically with relationships (prerequisites, related skills, proficiency levels), enabling precise skill identification without requiring complex custom matching algorithms for each job
Solution Approach 2:
The patent replaces traditional mechanical keyword-matching mechanisms with semantic analysis and natural language processing. Instead of simple string comparison, the system uses linguistic analysis to understand context, meaning, and relationships in job descriptions, significantly improving skill identification precision
2Measurement precision
If overly specific keywords are used in job descriptions, then the description is precise, but the candidate pool is reduced
Solution Approach 1:
The skill ontology serves multiple functions simultaneously: it provides precise skill definitions, establishes hierarchical relationships, identifies prerequisite skills, and enables flexible matching at different levels of specificity. This multi-functionality allows the system to maintain precision while expanding the effective candidate pool through hierarchical matching
Solution Approach 2:
The patent adds dimensional structure to skill descriptions through the ontology hierarchy. Instead of flat keywords, skills are organized in multiple dimensions (proficiency levels, prerequisite relationships, related skills), enabling matching to occur at different granularities and expanding the effective candidate pool
3Measurement precision
If manually created job descriptions are used, then the descriptions are tailored and accurate, but the process is time-consuming
Solution Approach 1:
The system performs preliminary semantic analysis on job descriptions to automatically extract and structure skill requirements before the matching process begins. By pre-processing and organizing skills into the ontology framework, the system eliminates manual skill identification time while maintaining accuracy
Solution Approach 2:
The skill ontology and semantic analysis enable the system to automatically generate structured skill descriptions from unstructured job texts without human intervention. The system serves itself by extracting, organizing, and standardizing skill information autonomously
4Reliability
If comprehensive skill analysis is performed, then the learning plan is complete, but the complexity of skill acquisition increases
Solution Approach 1:
The skill ontology segments comprehensive skill sets into hierarchical components with clear prerequisites and relationships. This segmentation breaks down complex skill acquisition into manageable stages, maintaining completeness while reducing perceived complexity for learners
Solution Approach 2:
The system dynamically adapts learning plans based on the ontology structure and individual candidate profiles. The hierarchical relationships in the ontology enable flexible path generation that adjusts to learner needs while maintaining comprehensive coverage of required skills
Data Source
AI summary
A computer-implemented method for generating a description of a target skill set using domain specific language, a computer program product, and a system. Embodiments may comprise, on a processor, ingesting a data set related to the target skill from a data store, semantically analyzing the data set to generate a skill ontology, generating a hyperplane to separate one or more priority skills from among the plurality of related skills, generating a description for the target skill from the one or more priority skills, and presenting the generated description to a user. The skill ontology may include relationships between the target skill and a plurality of related skills.