自然灾害应急知识图谱构建方法研究

学习应对火灾、地震等自然灾害的应对方法 #生活知识# #生活规划# #日常应急处理#

摘要: 中国自然灾害发生频繁，受自然灾害的威胁极大，防灾减灾、抗灾救灾是人类生存发展的永恒课题。在自然灾害应急领域中，相关数据骤增而应急关键知识明显匮乏，存在“数据-信息-知识”转化能力不足的问题，由此提出了自顶向下和自底向上相结合的自然灾害应急知识图谱构建方法。围绕自然灾害事件、灾害应急任务、灾害数据、模型方法4个要素，自顶向下构建模式层，通过本体建模形成知识图谱的概念框架；自底向上构建数据层，通过数据获取、知识抽取、融合、存储建立实体间关联关系。以洪涝灾害应急知识图谱为例进行实验验证，结果表明，该方法能够对自然灾害事件、灾害应急任务、灾害数据、模型方法4要素的概念层次关系及要素属性、要素间语义关联关系进行形式化表达，实现了从多源数据到互联知识的转化。

关键词: 自然灾害  /  应急  /  领域知识图谱  /  本体  

Abstract: Natural disasters occur frequently and pose a huge threat to China. Disaster prevention, mitigation, and disaster relief are eternal topics of human survival and development. However, in the field of disaster relief and emergency response, the relevant data increase sharply while the critical knowledge of emergency is obviously lacking. The "data-information-knowledge" transformation capacity is insufficient to meet the urgent needs of disaster prevention and reduction. Firstly taking natural disasters as the core, and around four elements of natural disaster events, disaster emergency tasks, disaster data, and methods, this paper proposes a knowledge graph construction method by combining a top-down approach and a bottom-up approach. Then, concept layer of knowledge graph is built from top to down, and the conceptual framework is formed through ontology modeling. Data layer of knowledge graph is built from bottom to top, and the relationship between entities is established through data acquisition, knowledge extraction, fusion, and storage. Finally, a flood disaster emergency knowledge graph is built to verify the validity of the proposed method. The concept layer in flood disaster emergency knowledge graph defines the conceptual levels, the attributes and the semantic relationships of flood disaster events, disaster emergency tasks, disaster data, and methods. The data layer in flood disaster emergency knowledge graph realizes the extraction of entities and relationships from multi-source data. After the knowledge fusion process, 3 054 nodes and 12 689 relationship edges are obtained and stored in the Neo4j graph database. The flood disaster emergency knowledge graph realizes the transformation from multi-source data to interrelated knowledge.

图  1   自然灾害应急知识图谱构建流程

Figure  1.   Construction Process of Knowledge Graph in Natural Disaster Emergency Field

图  2   本体之间语义关联关系

Figure  2.   Semantic Association Between Ontologies

图  3   洪涝灾害应急知识图谱模式层

Figure  3.   Ontology of Flood Disaster Emergency Knowledge Graph

图  4   洪涝灾害应急知识图谱数据层（部分）

Figure  4.   Instance of Flood Disaster Emergency Knowledge Graph(Part)

表  1   应急任务概念层次

Table  1   Levels of Concept for Emergency Tasks

过程 目标 具体应急任务 灾前 预警、预防、备灾 风险监测、风险评估、灾害预警等 灾中 快速反应、应急处置 应急响应级别、灾中快速评估、应急救助资源配置与调度决策、转移安置决策、应急推演等 灾后 恢复重建、总结评估 灾情综合评估、恢复重建效果评估等

表  2   灾害数据语义关系

Table  2   Semantic Relationships Between Disaster Data

名称 量化方法 说明 对应关系 时间重合度 ${\rm{Overla}}{{\rm{p}}_{T\left( {i, j} \right)}} = \frac{{T\left( i \right)\mathop \cap \nolimits T\left( j \right)}}{{T\left( i \right) \cup T\left( j \right)}}$ T (i)为灾害数据i的时间跨度，T (j)为灾害数据 j的时间跨度，两者的时间范围交集与并集之比则为灾害数据i、j之间的时间重合度 OverlapT(i, j)⊆[0, 1]，值为0时表示数据间不具有时间关联性；值为(0, 1]时，表示数据间具有时间关联性，值越大，则灾害数据之间的时间关联性越强 空间重合度 ${\rm{Overla}}{{\rm{p}}_{S\left( {i, j} \right)}} = \frac{{S\left( i \right)\mathop \cap \nolimits S\left( j \right)}}{{S\left( i \right)\mathop \cup \nolimits S\left( j \right)}}$ S (i)为灾害数据i的空间范围，S (j)为灾害数据j的空间范围，两者的空间范围交集与并集之比则为灾害数据i、j之间的空间重合度 OverlapS (i, j)⊆[0, 1]，值为0时表示数据间不具有空间关联性；值为(0, 1]时，表示数据间具有空间关联性，值越大，则灾害数据之间的空间关联性越强 数据关联度 ${\rm{Suppor}}{{\rm{t}}_{\left( X \right)}} = \frac{k}{n}$ 设X为一个灾害数据项集，k为X在总灾害数据案例集里出现的次数，n为灾害数据案例的总数，则数据项集X的支持度为Support(X)。设置最小支持度阈值，当某项集的支持度值高于该阈值时，则该项集为频繁项集 Confidence(A → B)⊆[0, 1]，值为0时表示两者不具有关联性；值为(0, 1]时，表示两者之间具有数据关联性，值越大，表示在使用了数据A的条件下，使用数据B的概率越大，数据关联性越强 ${\rm{Confidenc}}{{\rm{e}}_{\left( {A \to B} \right)}} = \\ \frac{{{\rm{Suppor}}{{\rm{t}}_{\left( {A\mathop \cup \nolimits B} \right)}}}}{{{\rm{Suppor}}{{\rm{t}}_{\left( A \right)}}}}$ 若灾害数据A在某一项集中出现，且在同样项集中一定存在灾害数据B，可将两者的关联规则表示为：A → B，规则A → B的置信度为Confidence(A → B)

表  3   模型方法实体识别实验结果

Table  3   Results of Method Entity Recognition Based on CRF

特征选择 滑动窗口大小 准确率/% 召回率/% F度量 单字 1 91.43 59.41 72.02 单字 2 92.14 59.84 72.55 单字 3 93.75 55.16 69.45 单字+词性 2 91.81 59.19 71.90

表  4   模型方法实体匹配规则

Table  4   Matching Rules of Method Entity

前缀词 中间连接词 后缀词 采用
应用
运用
利用
提出
︙ 、
和
与
及其
以及
︙ 方法
算法
函数
模型
技术
︙

表  5   不同方法实体识别对比

Table  5   Comparison with Different Methods of Entity Recognition

方法 准确率/% 召回率/% F度量 CRF 92.14 59.84 72.55 规则匹配 68.13 82.65 74.69 CRF+规则匹配 79.76 88.02 83.69

表  6   知识融合前后对比

Table  6   Differences Between Comparison Before and After Knowledge Fusion

融合前 融合后 小波分析，小波分析方法，小波变换方法 小波分析方法 聚类分析，层次聚类分析方法，聚类分析方法 层次聚类分析方法 城市洪涝模型，洪涝仿真模型，洪涝模拟方法，城市洪涝仿真模型，城市洪涝分析模型，城市洪涝模拟方法，洪涝分析 城市洪涝仿真模型 ︙ ︙

表  7   洪涝灾害应急知识图谱节点及关系统计数据

Table  7   Statistics of Nodes and Relationships in Flood Disaster Emergency Knowledge Graph

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