Research on enterprise knowledge service based on semantic reasoning and data fusion

Area of Science:

• Enterprise Risk Management
• Big Data Analytics
• Knowledge Management Systems

Background:

• Big data presents challenges in enterprise risk management due to massive, multisource, and heterogeneous information.
• Current risk management often suffers from insufficient knowledge fusion and low intelligence levels.

Purpose of the Study:

• To explore enterprise knowledge service models for rapid response to risk incidents.
• To clarify the elements of knowledge service models in risk management.
• To address challenges of multisource and heterogeneous enterprise risk information.

Main Methods:

• Utilizing semantic reasoning and data fusion for knowledge integration.
• Employing knowledge graph analysis methods.
• Decomposing the risk domain knowledge service process into prewarning, in-event response, and postevent summary stages.

Main Results:

• Construction of a three-level knowledge service model (acquisition-organization-application) for risk domains.
• Integration of empirical knowledge with data-driven approaches.
• Demonstration of semantic reasoning and data fusion for expressing and organizing knowledge needs.

Conclusions:

• The proposed model offers solutions for enterprise managers in risk management.
• It enhances the ability to rapidly respond to risk incidents.
• It provides new avenues for interdisciplinary knowledge service theory innovation.