深度学习驱动的大坝安全知识图谱构建方法OA

Integrating knowledge graphs as external knowledge bases can effectively mitigate knowledge hallucination issues in large language models within specialized domains.To this end,a knowledge graph construction method for dam safety diagnosis is proposed using deep learning.A dual-path collaborative modeling approach is implemented based on a hybrid framework.The schema layer is designed top-down,establishing a four-dimensional conceptual system encompassing monitoring locations,monitoring instruments,monitoring indicators,and safety evaluation.Meanwhile,domain ontology integrating concrete dams,earth-rock dams,slopes,discharge structures,and compre-hensive safety evaluation methods is built using the seven-step methodology.The data layer is generated bottom-up,with a targeted RoMBA-CRF-Joint joint extraction model developed.This model employs multi-level feature collabo-ration through RoBERTa-Mamba-BiLSTM and end-to-end decoding via CRF-relation extraction to achieve joint entity-relation extraction.The model extracted over 11,000 entities and 13,000 relations from normative texts,achieving an annotation accuracy exceeding 80%.Finally,visualization and semantic retrieval of the knowledge graph were implemented using the Neo4j open-source graph database,providing a verifiable knowledge foundation and technical support for future large language model-driven intelligent dam safety diagnosis.