摘要
混凝土坝施工管理知识多以文本的形式记录存储,具有数据量大、碎片化严重、层次性差等特点。本文从非结构化文本数据中智能挖掘施工知识,理清知识间的逻辑关系,提升知识的应用效率是混凝土坝施工管理面临的重要问题。本文提出一种混凝土坝施工管理知识图谱智能生成方法,将海量文本数据转化为可直接利用的知识。融合字词向量、BiLSTM-CRF(Bi-directional Long Short-Term Memory-Conditional Random Field)网络、Attention机制,建立混凝土坝施工管理实体智能识别模型,强化施工实体特征,获取混凝土坝施工管理文本中的实体词语。结合已识别的施工实体,定义实体间关系类型,利用互信息提取实体关系,组合形成施工知识链,构建混凝土坝施工管理知识图谱。该方法应用于实际混凝土坝施工管理文本分析中,经过计算得到混凝土坝施工管理实体智能识别模型的F1值为92.48%,优于其他实体识别模型;利用已识别实体间的关联关系,建立了混凝土坝施工管理知识图谱,形成基于知识图谱的施工知识检索机制,实现施工知识的快速提取,提高了施工知识的应用效率。
Most of the concrete dam construction management(CDCM)knowledge is recorded and stored in text form,which features a huge amount of data,severe fragmentation,and poor hierarchy.An important issue for the management is to mine construction knowledge from unstructured text data,clarify the logical relationship of knowledge,and improve the efficiency of knowledge application.This paper presents an intelligent method for generating CDCM knowledge graphs,or converting massive text data into directly usable knowledge.We combine word vectors,char vectors,a Bi-directional Long Short-Term Memory-Conditional Random Field(BiLSTM-CRF)network,and the attention mechanism;and build an intelligent model for CDCM entity recognition.It strengthens construction entity characteristics and extracts entity words from the management texts.Relationship types between entities are defined by the construction entities identified;mutual information is used to extract entity relationships and obtain construction knowledge chains;a CDCM knowledge graph is established by combining these chains.Application to a real management case shows this model gives an F1 value of 92.48%,outperforming other entity recognition models.Thus,this study demonstrates that the knowledge graphs can be established using the relationships between the construction entities recognized and that based on the graphs,a retrieval mechanism of the construction knowledge can be worked out for its rapid extraction and high application efficiency.
作者
沈扬
田丹
刘昊
任秋兵
张栋梁
李明超
SHEN Yang;TIAN Dan;LIU Hao;REN Qiubing;ZHANG Dongliang;LI Mingchao(China Three Gorges Corporation,Beijing 100038,China;State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300350,China;Power China Zhongnan Engineering Corporation Limited,Changsha 410014,China)
出处
《水力发电学报》
CSCD
北大核心
2022年第9期118-128,共11页
Journal of Hydroelectric Engineering
基金
国家自然科学基金(52179139)
湖北省水电工程施工与管理重点实验室开放基金(2020KSD05)。
