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Related Experiment Video

Updated: Jul 15, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A systematic knowledge graph-based smart management method for operations: A case study of standardized management.

Peihan Wen1, Yiming Zhao1, Jin Liu1

  • 1School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, PR China.

Heliyon
|October 2, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a knowledge graph approach to automate standardized routine operation management (SROM), enhancing efficiency and reducing human resources for businesses, especially SMEs.

Keywords:
Knowledge graphKnowledge managementOperation managementQuality management system

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Area of Science:

  • Knowledge Management
  • Artificial Intelligence
  • Business Process Management

Background:

  • Standardized Routine Operation Management (SROM) is crucial for enterprises.
  • Emerging smart management technologies offer potential for increased efficiency and resource savings, particularly for Small and Medium Enterprises (SMEs).

Purpose of the Study:

  • To propose a systematic knowledge-based smart management method for automating SROM activities.
  • To leverage knowledge graphs for transforming human operations into automatic responses.
  • To validate the method using ISO 9000 implementation management as a case study.

Main Methods:

  • Constructed an ontology model based on domain knowledge characteristics and stability.
  • Designed a hybrid knowledge graph construction and dynamic updating framework integrating semantic similarity and natural language processing.
  • Developed a question-answering and reasoning system for automated decision-making and feedback on ISO 9000 management.

Main Results:

  • Successfully transformed human activities into a knowledge graph-based automatic operation for ISO 9000 management.
  • Validated the SROM knowledge graph in a Chinese SME, enabling applications like question-answering, job recommendation, and conflict detection.
  • Demonstrated the method's effectiveness in supporting auxiliary decision-making, risk control, and operational analysis.

Conclusions:

  • The proposed knowledge graph method effectively automates SROM activities, enhancing efficiency and reducing reliance on human operations.
  • The approach is practical and generalizable for various business management optimization tasks.
  • Knowledge graph technology offers significant potential for smart management in SMEs.