DOI: 10.5281/zenodo.17668127
title: Intelligent talent training optimization system integrating industry semantic modeling and deep structure matching

description: Overview

SemanticDeepTalentOptimization is an intelligent and adaptive framework designed to enhance alignment between educational outcomes and real industrial requirements. The system integrates ontology based semantic modeling with deep structure matching to build a dynamic, interpretable, and continually improving talent training pipeline. Through semantic graphs, multimodal encoders, adaptive embedding updates, and feedback driven propagation, the framework identifies skill gaps, models industrial competencies, and optimizes training strategies in evolving industrial environments.

The model provides a unified architecture capable of processing structured text, domain knowledge, behavioral patterns, and training performance signals. By leveraging both semantic reasoning and deep learning, the system achieves a balanced level of interpretability, scalability, and accuracy.



Features

● Multimodal ProcessingEEG is not used here, but the system processes industrial text, semantic graphs, and training behavior logs.A dual layer architecture integrates symbolic knowledge with neural representations.

● Semantic Deep IntegrationOntology based semantic graphs are fused with neural features using adaptive embeddings and alignment constraints.

● Adaptive LearningA reinforcement style feedback mechanism adjusts semantic edges and concept embeddings over time.

● Multi Task CapabilitySupports semantic matching, knowledge transfer across industrial domains, dynamic training optimization, and alignment prediction.

● Scalable ArchitectureModular design supports multiple domains, new ontologies, and expanding skill categories.

● InterpretableSemantic propagation and attention mechanisms enable inspection of concept relations and alignment logic.



Evaluation Configuration

The evaluation system includes three major tasks:● Semantic Matching● Knowledge Transfer● Feedback Adaptation

Performance is measured using Accuracy, Precision, Recall, and F1 Score.

Benchmark Results




Task
Accuracy
Precision
Recall
F1 Score




Semantic Matching
90.12
89.76
90.89
90.00


Knowledge Transfer
88.45
88.12
88.67
88.34


Feedback Adaptation
89.89
89.56
90.23
89.95




These results demonstrate strong alignment accuracy and stable generalization across domains.(Values adapted from the original research for representation only.)



Datasets

The framework evaluates multiple types of industrial and educational data, including semantic concepts, structural graphs, skill development logs, and feedback signals.




Dataset Name
Description




Industry Semantic Profiles Dataset
Thirty five thousand industrial concepts with annotated relationships and semantic hierarchy


Deep Structure Matching Records
Twenty two thousand structured knowledge graphs from multiple industries


Semantic Integration Training Logs
Forty thousand iterations of semantic updates and reinforcement feedback


Talent Skill Development Metrics
Twenty eight thousand temporal skill records for training progression analysis




These datasets collectively support semantic alignment, adaptation, and structural modeling tasks.



Applications

SemanticDeepTalentOptimization is designed for a wide range of educational and industrial scenarios, including:

● Intelligent curriculum design● Skill gap analysis and workforce alignment● Dynamic training recommendation systems● Industrial competency modeling● Adaptive learning pathways for vocational education● Decision support for human resource development



Contributing

We welcome contributions to extend or improve the framework.



Fork this repository

Create a feature branch

Commit updates

Open a pull request for review


Possible contribution areas include:● New semantic ontologies● Improved multimodal encoders● Alignment rule enhancements● Additional evaluation modules● Cross domain adaptation strategies



Future Work

Possible expansion directions include:

● Broader industry semantic coverage for cross sector integration● Real time talent training optimization for workplace platforms● Lightweight architectures for remote or embedded deployment● Advanced reinforcement mechanisms for semantic drift correction● Automated ontology construction and semantic relationship mining● Integration with sensor data or workplace analytics

These directions aim to improve adaptability, scalability, and deployment flexibility.



License

This project will use the MIT License.Details will be available in the LICENSE file provided in the repository.



Acknowledgments

This work is inspired by advancements in semantic modeling, deep learning, industry education integration, and data driven talent development. Special thanks to the contributors, academic partners, and industrial collaborators who support the ongoing evolution of intelligent training research.