Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management
Source abstract
Abstract
Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order. Amidst the challenges posed by intricate and unpredictable risk factors, knowledge graph technology is effectively driving risk management, leveraging its ability to associate and infer knowledge from diverse sources. This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios. Firstly, employing bibliometric methods, the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs. In the succeeding section, systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs. Objectively comparing and summarizing the strengths and weaknesses of each method, we provide recommendations for addressing the existing challenges in the construction process. Subsequently, categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards, and furnishing a detailed exposition on the applicability of technical routes and methods. Finally, the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed, and relevant improvement suggestions were proposed. Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Graph Neural Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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