Article: A comprehensive management method of audit data based on knowledge graph Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.8 pp.20 - 35 Abstract: A comprehensive management method of audit data based on knowledge graph is proposed to solve the problems of low F1 value, long data update delay time, and low data coverage in traditional methods. First, webcrawling technology is used to automate the collection of audit data. Secondly, based on the pre-processed data, a BiLSTM CRF joint model is used to achieve audit entity recognition, and a graph convolutional network (GCN) is used to complete the relationship extraction task, thereby constructing an audit knowledge graph. Finally, an incremental learning mechanism is introduced to dynamically update the knowledge graph, and comprehensive management of audit data is achieved based on the updated knowledge graph. The experimental results show that the F1 value of the proposed method is between 0.81 and 0.89, the data update delay time is stable at 100-110 ms, and the data coverage reaches over 90% after 10 iterations and remains stable. Inderscience Publishers - linking academia, business and industry through research

Title: A comprehensive management method of audit data based on knowledge graph

Authors: Xuena Lin

Addresses: Finance and Economics, Xinxiang Vocational and Technical College, Xinxiang, 453006, China

Abstract: A comprehensive management method of audit data based on knowledge graph is proposed to solve the problems of low F1 value, long data update delay time, and low data coverage in traditional methods. First, webcrawling technology is used to automate the collection of audit data. Secondly, based on the pre-processed data, a BiLSTM CRF joint model is used to achieve audit entity recognition, and a graph convolutional network (GCN) is used to complete the relationship extraction task, thereby constructing an audit knowledge graph. Finally, an incremental learning mechanism is introduced to dynamically update the knowledge graph, and comprehensive management of audit data is achieved based on the updated knowledge graph. The experimental results show that the F1 value of the proposed method is between 0.81 and 0.89, the data update delay time is stable at 100-110 ms, and the data coverage reaches over 90% after 10 iterations and remains stable.

Keywords: knowledge graph; KG; audit data; entity recognition; relationship extraction; dynamically update.

DOI: 10.1504/IJBIDM.2026.153563

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.8, pp.20 - 35

Received: 30 May 2025
Accepted: 30 Sep 2025
Published online: 14 May 2026 *

Title: A comprehensive management method of audit data based on knowledge graph

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