Year: 2025
Type: conference-paper
Abstract: This research addresses the critical challenge of effective knowledge management within the complex domain of industrial technology, leveraging ontology to facilitate both explicit and tacit knowledge integration.It presents a novel approach to structuring and utilizing knowledge for enhanced efficiency and innovation in industrial settings.The objectives are: 1) to design a domain-specific ontology optimized for tacit knowledge representation and management, and 2) to develop a knowledge management system for industrial technology based on this ontology.The knowledge elicitation process yielded an ontology structured with 11 core classes (Technology, Organization, Process, Innovation Process, Project, Resource, Market Factor, Skill, Knowledge, Regulation, Performance Indicator, and Innovation Process) and their interrelationships, which offers a robust framework for knowledge representation.System evaluation, conducted across multiple trials, demonstrates high accuracy, consistently achieving results between 80% and 100%.Specifically, the mean accuracy across trials ranged from 83% to 87%, with limited outliers, indicating strong reliability.Furthermore, the ontology's completeness across 6 dimensions (Production Technology, Information Systems, Innovation, etc.) was evaluated, revealing distinct distribution patterns and a high degree of coverage in key areas.This research contributes a valuable and validated framework and system prototype for advanced knowledge management in industrial technology, with potential applications in areas such as Industry 4.0, digital transformation, and smart manufacturing, offering a pathway to improved decision-making, process optimization, and accelerated innovation cycles.
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411067858