Dynamic Vulnerability Knowledge Graph Construction via Multi-Source Data Fusion and Large Language Model Reasoning

With the increasing number of network security threats and the frequent occurrence of software vulnerability attacks, the effective management and large-scale retrieval of vulnerability data have become urgent needs. Existing vulnerability information is scattered across heterogeneous sources and is difficult to integrate, which in turn makes it hard for security analysts to quickly retrieve and analyze relevant security knowledge. To address this problem, this paper proposes a method to construct a vulnerability knowledge graph by integrating multi-source vulnerability data, combining graph embedding technology with large language model reasoning to aggregate, infer, and enrich vulnerability knowledge. Experiments demonstrated that our domain-tuned Bidirectional Long Short-Term Memory–Conditional Random Field (BiLSTM-CRF) named entity recognition (NER), enhanced with a cybersecurity dictionary, achieved a 90.1% F1-score for entity extraction. For link prediction, a hybrid Graph Attention Network fused with GPT-3 reasoning boosted Hits1 by 0.137, Hits3 by 0.116, and Hits10 by 0.101 over the baseline. These results confirm that our approach markedly enhanced entity identification and relationship inference, yielding a more complete and dynamically updatable cybersecurity knowledge graph.