Research on enterprise risk knowledge graph based on multi-source data fusion

Abstract

The construction of risk knowledge graphs aims at the effective organization and utilization of enterprise knowledge resources in big data environments. To address the problem of static mapping in existing enterprise knowledge graphs, this paper introduces the time dimension to describe the evolutionary characteristics of enterprise risk events, such as dynamics, suddenness and timeliness. Through information extraction, knowledge fusion, ontology construction and dynamic knowledge reasoning about risk knowledge, a bottom-up enterprise dynamic risk knowledge graph is systematically constructed. In the knowledge fusion link, aiming at the imbalanced classification problem for the entity samples of a data set, this paper proposes the ResNet dynamic knowledge reasoning method to improve the loss balance function of the Multi-Net model. The experiments show that the new model can effectively improve the accuracy of entity and relationship prediction. Finally, the knowledge graph is applied to an intelligent question-answering system.