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

In the era of big data, the field of enterprise risk is facing considerable challenges brought by massive multisource heterogeneous information sources. In view of the proliferation of multisource and heterogeneous enterprise risk information, insufficient knowledge fusion capabilities, and the low...

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Vydané v:Neural computing & applications Ročník 34; číslo 12; s. 9455 - 9470
Hlavní autori: Yang, Bo, Yang, Meifang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.06.2022
Springer Nature B.V
Predmet:
Artificial Intelligence
Big Data
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data integration
Data Mining and Knowledge Discovery
Decision making
Domains
Empirical analysis
Event handling
Image Processing and Computer Vision
Information sources
Knowledge
Knowledge acquisition
Knowledge management
Knowledge representation
Probability and Statistics in Computer Science
Reasoning
Risk management
S.I.: AI-based Web Information Processing
Semantics
Special Issue on AI-based Web Information Processing
Knowledge fusion
Enterprise knowledge service
Semantic reasoning
Risk management
ISSN:0941-0643, 1433-3058
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Shrnutí:In the era of big data, the field of enterprise risk is facing considerable challenges brought by massive multisource heterogeneous information sources. In view of the proliferation of multisource and heterogeneous enterprise risk information, insufficient knowledge fusion capabilities, and the low level of intelligence in risk management, this article explores the application process of enterprise knowledge service models for rapid responses to risk incidents from the perspective of semantic reasoning and data fusion and clarifies the elements of the knowledge service model in the field of risk management. Based on risk data, risk decision making as the standard, risk events as the driving force, and knowledge graph analysis methods as the power, the risk domain knowledge service process is decomposed into three stages: prewarning, in-event response, and postevent summary. These stages are combined with the empirical knowledge of risk event handling to construct a three-level knowledge service model of risk domain knowledge acquisition-organization-application. This model introduces the semantic reasoning and data fusion method to express, organize, and integrate the knowledge needs of different stages of risk events; provide enterprise managers with risk management knowledge service solutions; and provide new growth points for the innovation of interdisciplinary knowledge service theory.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-06382-z