Research on construction technology of artificial intelligence security knowledge graph - Xiaochen SHEN; Yinhui GE; Bo CHEN; Ling YU (网络与信息安全学报)
As a major strategic technology, artificial intelligence is developing rapidly while bringing numerous security risks.Currently, security data for artificial intelligence is collected from disparate sources and lacks standardized description, making it difficult to integrate and analyze effectively.To address this issue, a method for constructing an artificial intelligence security knowledge graph was proposed.The knowledge graph was used to integrate the current multi-source heterogeneous data, scientifically represent complex relationships of the data, mine potential value and form a domain knowledge base.In view of the diversity and correlation of concepts in the field of artificial intelligence security, a hierarchical structure of artificial intelligence security ontology was proposed to make the ontology structure more diversified and extensible, provide rule constraints for the process of knowledge graph construction, and form an artificial intelligence security knowledge base.To effectively utilize feature information and reduce noise interference, named entity recognition algorithm based on BiLSTM-CRF and relationship extraction algorithm based on CNN-ATT were adopted for information extraction.The constructed artificial intelligence security dataset was then used to verify the performance of the algorithm.Based on the proposed ontology, the multi-level visualization results of the artificial intelligence security knowledge graph were presented in 3D effect, effectively connecting the multi-source security data information.The experimental results show that the constructed knowledge graph meets the multi-dimensional evaluation criteria of accuracy, consistency, completeness, and timeliness, providing knowledge support for artificial intelligence security research.Overall, the proposed method can help address the complexity and heterogeneity of security data in artificial intelligence and provide a more standardized, integrated approach to knowledge representation and analysis.