Job Description Generation Using Skill Knowledge Graphs
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Solution Overview
Problem
Conventional job description generation methods, whether manual or automated, often lack focus, accuracy, and comprehensiveness, leading to incomplete or inaccurate representation of required skills, which reduces the chances of attracting well-qualified candidates.
Innovation Solution
A machine learning-based approach that analyzes a dataset of job descriptions using syntactic and semantic natural language parsing to identify section-specific and job category-specific selective phrases, enabling the automatic generation of tailored job descriptions that accurately communicate job requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual job description writing is used, then the process allows human judgment and flexibility, but the accuracy and comprehensiveness of skill identification deteriorates due to writer unfamiliarity with skills ontologies
Solution Approach 1:
The patent introduces an intermediary system comprising a skills ontology database and natural language processing algorithms that mediate between the job description writer and the final job description. This intermediary automatically identifies and suggests relevant skills based on the written content, bridging the gap between human writing capability and expert-level skill identification accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual skill identification with an automated computational system using natural language processing and machine learning. The system automatically analyzes the job description text, extracts relevant information, and identifies required skills without relying on the writer's expertise in skills ontologies.
2Productivity
If automatic text generation systems are used, then the generation speed increases, but the quality of text deteriorates due to repetitions and nonsense in longer passages
Solution Approach 1:
The patent segments the job description generation process into distinct components: template selection, skill identification, content generation for specific sections, and formatting. By dividing the task into manageable segments with specialized handling for each, the system maintains high quality in each section while achieving overall fast generation.
Solution Approach 2:
The patent performs preliminary actions by pre-defining job description templates, skill taxonomies, and section structures before generation. This preparation enables the system to quickly assemble high-quality job descriptions by filling predefined structures with context-appropriate content rather than generating text from scratch.
3Adaptability or versatility
If manual job description writing is used, then the writer can exercise creativity and adaptability, but the time required for writing increases due to the need to identify all pertinent skills
Solution Approach 1:
The patent implements feedback mechanisms where the system analyzes the generated job description, identifies missing or insufficient skill descriptions, and provides suggestions for improvement. This iterative feedback loop maintains adaptability and comprehensiveness while reducing the time investors need to spend on skill identification.
Solution Approach 2:
The patent enables the job description generation system to serve itself by automatically identifying skills, selecting appropriate templates, and formatting content without continuous human intervention. The system uses its own built-in skills ontology and natural language processing capabilities to perform tasks that would otherwise require external expert input.
Data Source
AI summary
Techniques are described for automatically generating job descriptions. Training data is used to define job categories, job sections, and selective phrases for each job category and job section. In response to receiving request to generate a job description, a job category or additional information is derived from the request. Such information is used to populate existing templates for a job description based on a skill knowledge graph, which connects skill names based on hypernym trees and enables enhancement of a job description with better or broader coverage.