AI Semantic Skill Matching for Unstructured Talent Data
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Solution Overview
Problem
Current methods for discovering candidate information in talent acquisition, such as keyword search and deep learning pattern recognition, face challenges in accuracy and bias, leading to inefficient resource utilization and biased matching.
Innovation Solution
A multi-stage AI-based semantic technique is employed to extract skills from unstructured data by determining normalized roles and tasks based on semantic similarity scores, generating structured skill data for accurate and efficient matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword search approach is used, then implementation simplicity is improved, but matching accuracy deteriorates due to reliance on exact entities in predetermined dictionaries
Solution Approach 1:
The patent replaces the mechanical keyword matching system with a neural network-based semantic understanding system. The neural network processes raw text data to extract skills and generate embeddings, enabling semantic similarity matching rather than exact keyword matching. This substitution resolves the contradiction by achieving high accuracy without requiring complex predetermined dictionaries.
Solution Approach 2:
The patent changes the fundamental parameter of matching from exact string equality to semantic similarity based on neural network embeddings. By transforming text into vector representations and using cosine similarity or other distance metrics, the system achieves accurate matching without relying on predetermined keyword dictionaries, thus resolving the accuracy-simplicity contradiction.
2Measurement precision
If deep learning pattern recognition is used, then matching accuracy is improved, but unintentional bias toward certain demographics is introduced
Solution Approach 1:
The patent extracts and focuses exclusively on skill-related information from candidate profiles by using neural networks to identify and embed only the relevant skill descriptors. By filtering out demographic information and other non-skill attributes before the matching process, the system achieves accurate skill-based matching while eliminating the source of unintentional bias against certain demographics.
Solution Approach 2:
The patent introduces skill embeddings as an intermediary representation between raw candidate data and matching decisions. Instead of directly comparing candidate profiles that may contain biased demographic information, the system creates a neutral skill embedding space where matching is based solely on extracted skills, thus achieving accuracy without bias.
3Quantity of substance
If continuous data collection is performed, then data abundance is improved, but information utilization lags behind due to manual processing requirements
Solution Approach 1:
The patent implements a self-service automated processing system where neural networks continuously extract skills, generate embeddings, and perform matching without human intervention. The system automatically processes new candidate data as it arrives, utilizing the abundant data in real-time rather than requiring manual processing, thus resolving the contradiction between data abundance and utilization rate.
Solution Approach 2:
The patent establishes a continuous automated pipeline where data collection, skill extraction, embedding generation, and matching occur continuously without interruption. The neural network processes data streams in real-time, maintaining continuous useful action that keeps pace with data collection rates, thereby resolving the lag between data abundance and information utilization.
Data Source
AI summary
Techniques for extracting skills from unstructured raw data are provided. The method includes determining at least one normalized role that match a role of the raw data, wherein the match is determined based on a first semantic similarity score generated for each of normalized roles of a set of normalized roles; generating a subset of normalized tasks that are associated with the at least one normalized role; determining, based on a second semantic similarity score, at least one normalized task for a task data unit of the raw data, wherein the task data unit is a portion of the raw data that describes tasks of the role, wherein the at least one normalized task is a normalized task in the subset of normalized tasks; aggregating skills that are associated with the at least one normalized task; and generating structured skill data of the raw data using the aggregated skills.