[1]CUI Tiejun,LI Shasha.Causality extraction of SFEP text and its conversion to SFN[J].CAAI Transactions on Intelligent Systems,2020,15(5):998-1005.[doi:10.11992/tis.201907021]
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Causality extraction of SFEP text and its conversion to SFN

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 998-1005 Column: 学术论文—知识工程 Public date: 2020-09-05
Title:
Causality extraction of SFEP text and its conversion to SFN
Author(s):
CUI Tiejun1; 2;  LI Shasha2
1. College of Safety Science and Engineering, Liaoning Technical University, Fuxin 123000, China;
2. School of Business Administration, Liaoning Technical University, Huludao 125105, China
Keywords:
safety system engineering; intelligence science; knowledge extraction; system fault; evolution process; text causality; space fault network; transformation research
CLC:
X913； C931.1；TP18
DOI:
10.11992/tis.201907021
Abstract:
To transform the text description of the system fault evolution process (SFEP) into a space fault network (SFN) for fault analyses, we propose a method for extracting the text causality of SFEP and its transformation with the basic structure of SFN. First, several typical causal relationships of events in SFEP are given. Then, the transformation process of causality and SFN basic structure is proposed. The method focuses on the key words and causal group models; the keywords and group models are enriched and supplemented through continuous model learning. Finally, the method transforms SFEP text into an SFN structure. The method is applied to the text processing of aircraft landing gear failure. The findings show that the method can be used to analyze causality in SFEP text and obtain an ideal SFN. Perfect keywords and group models are beneficial to the intelligent processing of SFEP text to SFN using computers.
References:
[1] 崔铁军, 李莎莎, 朱宝艳. 含有单向环的多向环网络结构及其故障概率计算[J]. 中国安全科学学报, 2018, 28(7): 19-24
CUI Tiejun, LI Shasha, ZHU Baoyan. Multidirectional ring network structure with one-way ring and its fault probability calculation[J]. China safety science journal, 2018, 28(7): 19-24
[2] CUI Tiejun, LI Shasha. Research on basic theory of space fault network and system fault evolution process[J]. Neural computing and applications, 2020, 32(6): 1725-1744.
[3] 崔铁军, 李莎莎. 空间故障树与空间故障网络理论综述[J]. 安全与环境学报, 2019, 19(2): 399-405
CUI Tiejun, LI Shasha. Revision of the space fault tree and the space fault network system[J]. Journal of safety and environment, 2019, 19(2): 399-405
[4] 崔铁军, 李莎莎, 朱宝艳. 空间故障网络及其与空间故障树的转换[J]. 计算机应用研究, 2019, 36(8): 2000-2003
CUI Tiejun, LI Shasha, ZHU Baoyan. Construction space fault network and recognition network structure characteristic[J]. Application research of computers, 2019, 36(8): 2000-2003
[5] CUI Tiejun, LI Shasha. Deep learning of system reliability under multi-factor influence based on space fault tree[J]. Neural computing and applications, 2019, 31(9): 4761-4776.
[6] 崔铁军, 马云东. DSFT的建立及故障概率空间分布的确定[J]. 系统工程理论与实践, 2016, 36(4): 1081-1088
CUI Tiejun, MA Yundong. Discrete space fault tree construction and failure probability space distribution determination[J]. Systems engineering-theory and practice, 2016, 36(4): 1081-1088
[7] 崔铁军, 马云东. 离散型空间故障树构建及其性质研究[J]. 系统科学与数学, 2016, 36(10): 1753-1761
CUI Tiejun, MA Yundong. Discrete space fault tree construction and application research[J]. Journal of systems science and mathematical sciences, 2016, 36(10): 1753-1761
[8] 崔铁军, 马云东. DSFT中因素投影拟合法的不精确原因分析[J]. 系统工程理论与实践, 2016, 36(5): 1340-1345
CUI Tiejun, MA Yundong. Inaccurate reason analysis of the factors projection fitting method in DSFT[J]. Systems engineering-theory & practice, 2016, 36(5): 1340-1345
[9] CUI Tiejun, LI Shasha. Study on the construction and application of discrete space fault tree modified by fuzzy structured element[J]. Cluster computing, 2019, 22(3): 6563-6577.
[10] 崔铁军, 马云东. DSFT下模糊结构元特征函数构建及结构元化的意义[J]. 模糊系统与数学, 2016, 30(2): 144-151
CUI Tiejun, MA Yundong. The construction of fuzzy structured element characteristic function and the significance of structure elemented in DSFT[J]. Fuzzy systems and mathematics, 2016, 30(2): 144-151
[11] 崔铁军, 马云东. 多维空间故障树构建及应用研究[J]. 中国安全科学学报, 2013, 23(4): 32-37, 62
CUI Tiejun, MA Yundong. Research on multi-dimensional space fault tree construction and application[J]. China safety science journal, 2013, 23(4): 32-37, 62
[12] 崔铁军, 李莎莎, 马云东, 等. 基于ANN求导的DSFT中故障概率变化趋势研究[J]. 计算机应用研究, 2017, 34(2): 449-452
CUI Tiejun, LI Shasha, MA Yundong, et al. Research on method for trend of failure probability in DSFT based on ANN derivation[J]. Application research of computers, 2017, 34(2): 449-452
[13] 崔铁军, 汪培庄, 马云东. 01SFT中的系统因素结构反分析方法研究[J]. 系统工程理论与实践, 2016, 36(8): 2152-2160
CUI Tiejun, WANG Peizhuang, MA Yundong. Inward analysis of system factor structure in 01 space fault tree[J]. Systems engineering-theory and practice, 2016, 36(8): 2152-2160
[14] 崔铁军, 马云东. 基于因素空间中属性圆对象分类的相似度研究及应用[J]. 模糊系统与数学, 2015, 29(6): 56-63
CUI Tiejun, MA Yundong. Research on the similarity of object classification of attribute circular and application based on factors space[J]. Fuzzy systems and mathematics, 2015, 29(6): 56-63
[15] LI Shasha, CUI Tiejun, LIU Jian. Study on the construction and application of cloudization space fault tree[J]. Cluster computing, 2019, 22(3): 5613-5633.
[16] 崔铁军, 李莎莎, 马云东, 等. SFT下云化因素重要度和因素联合重要度的实现与认识[J]. 安全与环境学报, 2017, 17(6): 2109-2113
CUI Tiejun, LI Shasha, MA Yundong, et al. Identification and realization of the cloud factor significance and the factor integration significance of the space fault tree[J]. Journal of safety and environment, 2017, 17(6): 2109-2113
[17] CUI Tiejun, WANG Peizhuang, LI Shasha. The function structure analysis theory based on the factor space and space fault tree[J]. Cluster computing, 2017, 20(2): 1387-1399.
[18] 崔铁军, 李莎莎, 王来贵. 完备与不完备背景关系中蕴含的系统功能结构分析[J]. 计算机科学, 2017, 44(3): 268-273, 306
CUI Tiejun, LI Shasha, WANG Laigui. System function structure analysis in complete and incomplete background relationship[J]. Computer science, 2017, 44(3): 268-273, 306
[19] 崔铁军, 李莎莎, 王来贵. 基于属性圆的多属性决策云模型构建与可靠性分析应用[J]. 计算机科学, 2017, 44(5): 111-115
CUI Tiejun, LI Shasha, WANG Laigui. Multi-attribute decision making model based on attribute circle and application of reliability analysis[J]. Computer science, 2017, 44(5): 111-115
[20] 崔铁军, 李莎莎, 马云东, 等. 不同元件构成系统中元件维修率分布确定[J]. 系统科学与数学, 2017, 37(5): 1309-1318
CUI Tiejun, LI Shasha, MA Yundong, et al. Determine component maintenance rate distribution in the system with different components[J]. Journal of systems science and mathematical sciences, 2017, 37(5): 1309-1318
[21] 张文辉, 赵文光. 基于数据挖掘的药物不良反应因果关系研究[J]. 中国数字医学, 2019, 14(5): 43-45
ZHANG Wenhui, ZHAO Wenguang. Research on the causal relationship of adverse drug reactions based on data mining[J]. China digital medicine, 2019, 14(5): 43-45
[22] 舒晓灵, 陈晶晶. 重新认识“数据驱动”及因果关系——知识发现图谱中的数据挖掘研究[J]. 中国社会科学评价, 2017(3): 28-38
SHU Xiaoling, CHEN Jingjing. Reconsidering ‘data driven’ and causal relationship: data mining in the spectrum of knowledge discovery in database[J]. Social sciences in China review, 2017(3): 28-38
[23] 李冰, 陈奡, 张永伟. 基于知识地图的文本分类方法[J]. 指挥信息系统与技术, 2018, 9(1): 92-95
LI Bing, CHEN Ao, ZHANG Yongwei. Text classification method based on knowledge map[J]. Command information system and technology, 2018, 9(1): 92-95
[24] 潘洋彬. 基于知识图谱的文本分类算法研究[D]. 厦门: 厦门大学, 2018.
PAN Yangbin. Research on text classification algorithm based on knowledge graph[D]. Xiamen: Xiamen University, 2018.
[25] 方欢, 张源, 吴其林. 一种基于结构因果关系和日志变化挖掘的BPMSs故障诊断方法[J]. 控制理论与应用, 2018, 35(8): 1167-1176
FANG Huan, ZHANG Yuan, WU Qilin. A log induced change mining method for fault diagnosis using structure causality in BPMSs[J]. Control theory and applications, 2018, 35(8): 1167-1176
[26] 何绯娟, 石磊, 缪相林. MOOC学习行为数据中因果关系的挖掘方法[J]. 信息与电脑(理论版), 2018(21): 129-131
HE Feijuan, SHI Lei, MIAO Xianglin. Mining causality from learning behavior in MOOC[J]. China computer and communication, 2018(21): 129-131
[27] QIU Jiangnan, XU Liwei, ZHAI Jie, et al. Extracting causal relations from emergency cases based on conditional random fields[J]. Procedia computer science, 2017, 112: 1623-1632.
[28] 佘青山, 陈希豪, 高发荣, 等. 基于感兴趣脑区LASSO-Granger因果关系的脑电特征提取算法[J]. 电子与信息学报, 2016, 38(5): 1266-1270
SHE Qingshan, CHEN Xihao, GAO Farong, et al. Feature extraction of electroencephalography based on LASSO-Granger causality between brain region of interest[J]. Journal of electronics and information technology, 2016, 38(5): 1266-1270
[29] HUNG Yingchao, TSENG N. Extracting informative variables in the validation of two-group causal relationship[J]. Computational statistics, 2013, 28(3): 1151-1167.
[30] 刘现营. 面向医疗知识的PDF文本内容提取系统设计与实现[D]. 哈尔滨：哈尔滨工业大学, 2018.
LIU Xianying. Design and implementation of PDF text content extraction system for medical knowledge[D]. Harbin: Harbin Institute of Technology, 2018.
[31] 黄新平. 政府网站信息资源多维语义知识融合研究[D]. 长春: 吉林大学, 2017.
HUANG Xinping. Study on multi-dimensional semantic knowledge fusion of government website information resources[D]. Changchun: Jilin University, 2017.
[32] 唐静华. 基于特征项权重与句子相似度的知识元智能提取技术研究[D]. 成都: 西南交通大学, 2017.
TANG Jinghua. Research on intelligent extraction of knowledge element based on feature item weight and sentence similarity[D]. Chengdu: Southwest Jiaotong University, 2017.
[33] 李悦群, 毛文吉, 王飞跃. 面向领域开源文本的因果知识提取[J]. 计算机工程与科学, 2010, 32(5): 100-104
LI Yuequn, MAO Wenji, WANG Feiyue. Causal knowledge extraction based on open source domain texts[J]. Computer engineering and science, 2010, 32(5): 100-104
[34] 何华灿. 重新找回人工智能的可解释性[J]. 智能系统学报, 2019, 14(3): 393-412
HE Huacan. Refining the interpretability of artificial intelligence[J]. CAAI transactions on intelligent systems, 2019, 14(3): 393-412
[35] 何华灿. 泛逻辑学理论——机制主义人工智能理论的逻辑基础[J]. 智能系统学报, 2018, 13(1): 19-36
HE Huacan. Universal logic theory: logical foundation of mechanism-based artificial intelligence theory[J]. CAAI transactions on intelligent systems, 2018, 13(1): 19-36
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