AIR: Adaptive Incremental Embedding Updating for Dynamic Knowledge Graphs

Jia, Zhang; Li, Haoyang; Chen, Lei

doi:10.1007/978-3-031-30672-3_41

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Developing efficient data updating and maintenance mechanisms ensures that knowledge graphs can promptly reflect data changes and support real-time processing. Jia et al [158] proposed an adaptive incremental update embedding framework for dynamic knowledge graphs, dynamically updating and maintaining knowledge graphs based on a performance review mechanism. Utilizing cloud computing resources to dynamically adjust resource allocation according to demand supports the dynamic expansion of knowledge graphs.…”

Section: Scalability Issues In Knowledge Graphsmentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

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In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing.

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Section: Scalability Issues In Knowledge Graphsmentioning

confidence: 99%