Abstract—The paper presents a novel method for constructing a multi-domain, multi-layer cybersecurity semantic knowledge base. Utilizing diverse cybersecurity data sources, the framework seamlessly integrates entity extraction, event extraction, and semantic relation extraction through sophisticated semantic parsing techniques, resulting in a highly accurate and comprehensive knowledge base. By employing Bi-directional Long Short-Term Memory (Bi-LSTM) models with attention mechanisms for precise entity extraction, a hierarchical strategy network for detailed event extraction, and pattern rule matching for elaborate semantic relation extraction, the proposed method demonstrates exceptional efficacy. Experimental results show entity extraction with 95.4% accuracy and 92.5% recall, event extraction with 93.3% accuracy and 90.8% recall, and semantic relation extraction with 90.7% accuracy and 88.6% recall. The constructed knowledge base achieves an average query response time of 0.5 s and a query accuracy of 92%. This innovative approach enhances the processing and understanding of complex cybersecurity data, providing reliable and precise semantic knowledge query and reasoning, crucial for dynamic threat response.
 
Keywords—semantic knowledge base, cybersecurity, semantic parsing, attention mechanism, hierarchical policy networks


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