Dynamic Assessment and Early Warning of Cross-Border Pipeline Risks Based on Knowledge Graph

This paper introduces a novel framework for the dynamic assessment and early warning of cross-border pipeline risks, leveraging the zero-shot learning capabilities of the GPT-4 large language model (LLM) to construct and utilize a domain-specific knowledge graph. The approach circumvents the need for extensive pre-training or fine-tuning, thereby enabling rapid development and deployment in a domain characterized by data scarcity. The methodology begins with the construction of a knowledge graph, which involves a comprehensive ontology design, followed by knowledge extraction from unstructured text sources, and culminates in knowledge representation and storage in a structured format. Subsequently, the framework facilitates dynamic risk understanding and analysis, encompassing risk factor identification, risk event inference, and the analysis of complex relationships between these elements, all supported by the structured knowledge base. Finally, the system provides knowledge-based risk interpretation and decision-making support, generating explanations for risk events, assessing their potential impacts, and offering actionable recommendations to stakeholders. By employing carefully crafted prompts and the inherent natural language processing capabilities of GPT-4, the framework facilitates the extraction of entities and relationships from unstructured textual data, effectively populating the knowledge graph. This knowledge graph, representing entities, attributes, and relationships pertinent to cross-border pipeline risks, serves as a foundation for dynamic risk assessment. This method presents a scalable, data-efficient approach tailored to the specialized domain of cross-border pipeline risk management, particularly advantageous in contexts with limited resources. The proposed framework demonstrates the potential of LLMs, specifically GPT-4, in conjunction with knowledge graph methodologies, to advance risk management practices in complex, data-sparse domains.