Machine-Learning Skill Data Generation Via Semantic Knowledge Graphs
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Solution Overview
Problem
Existing systems struggle to effectively search and generate skill-related data due to the large number of skill names with similar or identical meanings, making it difficult to find suitable candidates for jobs and create relevant job descriptions or resumes.
Innovation Solution
A data management server organizes skill names using machine learning techniques to create dependency graphs and hypernym trees, forming a knowledge graph that connects skill names semantically, allowing for efficient search and generation of skill-related data such as job descriptions and resumes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If skill names are organized using traditional methods, then the system can store skill data, but search accuracy and relevance deteriorate due to hundreds of thousands of skill names with identical or similar meanings
Solution Approach 1:
The patent merges skill names that have identical or similar meanings by organizing them into a knowledge graph where semantically equivalent skills are connected. This consolidation reduces the effective number of distinct skill entities while maintaining comprehensive coverage, thereby improving search accuracy without losing skill data diversity.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary structure between traditional skill databases and search queries. This knowledge graph mediates by establishing semantic relationships and connections between skill names, enabling the system to understand and differentiate between similar skills, thus improving search precision despite the large volume of skill names.
2Adaptability or versatility
If the system stores comprehensive skill data, then coverage is improved, but search efficiency and speed deteriorate due to the large volume of data
Solution Approach 1:
The patent segments the large volume of skill data into a structured knowledge graph with nodes representing skills and edges representing relationships. This segmentation organizes the comprehensive skill data into manageable, interconnected units that can be efficiently traversed and queried, maintaining full coverage while improving search efficiency through structured access patterns.
Solution Approach 2:
The patent adds a semantic dimension to skill data storage by organizing skills in a knowledge graph that captures relationships and meanings beyond simple categorical classification. This dimensional transformation enables the system to maintain comprehensive skill coverage while allowing searches to leverage semantic connections, thereby improving efficiency through smarter data organization rather than brute-force searching.
3Measurement precision
If the system uses detailed skill names, then specificity is improved, but the ability to find related skills and generate relevant content deteriorates
Solution Approach 1:
The patent creates a universal knowledge graph structure that serves multiple functions simultaneously: it preserves detailed skill name specificity through node labels while enabling relationship discovery through edge connections. This multi-functional design allows the same structure to support both precise skill identification and broader skill relationship exploration, generating relevant content across different contexts.
Solution Approach 2:
The patent implements feedback mechanisms where the knowledge graph structure provides information about skill relationships that feeds back into the search and content generation processes. This feedback enables the system to use detailed skill name information not just for precise matching but also for discovering related skills through the accumulated relationship data, thereby enhancing both specificity and adaptability.
Data Source
AI summary
A method comprises obtaining a hypernym tree for a word selected from each skill name of a plurality of skill names, the obtained hypernym tree including a node for each meaning of the word, each node of an obtained hypernym tree for a meaning of a word including one or more synonyms corresponding to the meaning and a summary description of the meaning, at least one hypernym tree including nodes for meanings of different words; creating an embedding for each skill name and for each synonym and the summary description included in a node of each obtained hypernym tree computing, a distance between the embedding for a skill name and the embedding for each synonym and summary description included in a node of an obtained hypernym tree; assigning to the skill name a meaning of a word associated with a node of any obtained hypernym tree that leads to a lowest distance.