RoGloRE: A Global-Local Adaptive Joint Knowledge Extraction Framework for Chinese Cyber Threat Intelligence
Automated parsing of Cyber Threat Intelligence (CTI) is crucial for threat attribution and proactive defense. However, Chinese CTI texts are highly unstructured and semantically fragmented, posing dual challenges for existing models. In entity extraction, fragmented tokenization caused by high-entropy entities and complex nested structures leads to ambiguous entity boundaries. In relation extraction, critical attack clues are scattered across paragraphs, preventing traditional attention mechanisms from effectively capturing long-range dependencies and relative spatial structures. To address these limitations, we propose an adaptive global-local joint extraction framework designed for fragmented semantic aggregation in Chinese CTI. Within the entity recognition module, we introduce adaptive rotary position embeddings to correct low-level positional features. This mechanism, combined with a type-decoupled GlobalPointer, resolves recognition conflicts involving long-span entities and nested boundaries. In the relation extraction module, we design a dual-stage attention mechanism to dynamically integrate global cross-paragraph spatial clues with local entity neighborhood features. Additionally, an adaptive decoding strategy aware of class imbalance is implemented to enhance the robustness of the model against sparse long-tail relations. Experimental results on the CDTier dataset indicate that the proposed framework achieves a 9.45% improvement in the F1 score of entity extraction over the best existing baseline, alongside a precision of 93.3% and a recall of 95.5% for relation extraction. The proposed method overcomes the bottleneck of long-range semantic parsing in complex Chinese contexts, demonstrating superior generalization capabilities and practical utility.