装备-标准知识图谱的过程建模研究

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 502-505.

装备-标准知识图谱的过程建模研究

尹亮¹,何明利¹,谢文波²,陈端兵^2,3,4

装甲兵工程学院北京100072¹
电子科技大学计算机科学与工程学院成都611731²
电子科技大学大数据研究中心成都611731³
电子科技大学数字文化与传媒研究中心成都611731⁴

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:尹亮(1982－),男,博士生,工程师,主要研究方向为指控通信与装甲车辆工程,E-mail:1804359156@qq.com;何明利(1963－),男,硕士,高级工程师,博士生导师,主要研究方向为指控通信与装甲车辆工程;谢文波(1990－),男,博士生,主要研究方向为数据挖掘、数据建模;陈端兵(1971-),男,博士,副教授,硕士生导师,主要研究方向为复杂网络分析、数据建模、大数据挖掘。
基金资助:
国家自然科学基金(61433014,61673085),中央高校基本科研业务费专项资金(ZYGX2014Z002)资助

Process Modeling on Knowledge Graph of Equipment and Standard

YIN Liang¹,HE Ming-li¹,XIE Wen-bo²,CHEN Duan-bing^2,3,4

The Academy of Armored Forces Engineering,Beijing 100072,China¹
School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China²
Center for Big Data,University of Electronic Science and Technology of China,Chengdu 611731,China³
The Center for Digitized Culture and Media,University of Electronic Science and Technology of China,Chengdu 611731,China⁴

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 为了清晰地描述装备、标准以及标准化要素之间的复杂联系,构建装备-标准知识图谱是一种重要的分析手段。利用装备-标准知识图谱,可实现标准化研究从型号跟随到体系引领、从定性分析到定量分析、从单项评审到系统验证的转变,而过程建模是构建装备-标准知识图谱的核心环节之一。文中采用IDEF3建模方法,对装备-标准知识图谱的整体架构以及图谱中涉及到的各个子过程进行了建模分析。通过过程建模,得到了装备-标准知识图谱的异质网络模型。

关键词: 过程建模, 异质网络模型, 知识图谱

Abstract: In order to clearly describe the complex association between equipment,standards,and standardized elements,it is an importantly analytical tool to construct a knowledge graph of equipment-standard.Using the constructed knowledge graph of equipment-standard,the transformation of standardization research can be achieved from model following to system leading,from qualitative analysis to quantitative analysis,and from individual evaluation to system ve-rification.The process modeling is a key step in the knowledge graph modeling.The IDEF3 method is applied to model the main structure of knowledge graph and the sub-processes involved.A heterogeneous network model of equipment-standard knowledge graph is obtained through process modeling.

Key words: Heterogeneous network model, Knowledge graph, Process modeling

中图分类号:

TP391

尹亮,何明利,谢文波,陈端兵. 装备-标准知识图谱的过程建模研究[J]. 计算机科学, 2018, 45(6A): 502-505. https://doi.org/

YIN Liang,HE Ming-li,XIE Wen-bo,CHEN Duan-bing. Process Modeling on Knowledge Graph of Equipment and Standard[J]. Computer Science, 2018, 45(6A): 502-505. https://doi.org/

参考文献

[1]LIN Y,LIU Z,SUN M,et al.Learning entity and relation embeddings for knowledge graph completion[C]∥Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.2015:2181-2187.
[2]JI G,LIU K,HE S,et al.Knowledge graph Completion with adaptive sparse transfer matrix[C]∥Proceedings of the Thir-tieth AAAI Conference on Artificial Intelligence.2016:985-991.
[3]YANG S,ZOU L,WANG Z,et al.Efficiently answering technical questions-A knowledge graph approach[C]∥Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.2017:3111-3118.
[4]HEIKO P.Knowledge graph refinement:A survey of approaches and evaluation methods[J].Semantic Web,2017,8(3):489-508.
[5]NIKOLAEV F,KOTOV A,ZHILTSOV N.Parameterized fielded term dependence models for Ad-hoc entity retrieval from knowledge graph[C]∥Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval.Pisa,Italy,2016:435-444.
[6]BRACHMAN R J.What IS-A is and isn’t:An analysis of taxonomic links in semantic networks [J].Computer,1983,10:30-36.
[7]STEINER T,VERBORGH R,TRONCY R,et al.Adding realtime coverage to the Google knowledge graph[C]∥Proceedings of the 2012th International Conference on Posters & Demonstrations Track-Volume 914.CEUR-WS.org,2012:65-68.
[8]WANG Z,WANG Z,LI J,et al.Knowledge extraction from Chinese wiki encyclopedias[J].Journal of Zhejiang University Scien-ce C,2012,13(4):268-280.
[9]ZENG Y,WANG H,HAO H,et al.Statistical and structural analysis of web-based collaborative knowledge bases generated from Wiki Encyclopedia[C]∥Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01.IEEE Computer Society,2012:553-557.
[10]HUANG Z,CHUANG W,ONG T H,et al.A graph-based re- commender system for digital library[C]∥Proceedings of the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries.ACM,2002:65-73.
[11]SIEK J G,LEE L Q,LUMSDAINE A.The boost graph library:user guide and reference manual,portable documents[M].Pearson Education,2001.
[12]DAI X,LI J,LIU T,et al.HRGRN:A graph search-empowered integrative database of Arabidopsis signaling transduction,metabolism and gene regulation networks[J].Plant and Cell Phy-siology,2016,57(1):e12.
[13]刘峤,李杨,杨段宏,等.知识图谱构建技术综述[J].计算机研究与发展,2016,53(3):582-600.
[14]徐增林,盛泳潘,贺丽荣,等.知识图谱技术综述[J].电子科技大学学报,2016,45(4):589-606.
[15]耿霞,张继军,李蔚妍.知识图谱构建技术综述[J].计算机科学,2014,41(7):148-152.
[16]刘昶,史海波,于海斌.基于多Agent制造过程的建模方法[J].控制工程,2005,12(6):515-519.
[17]高飞,高阜乡,王钰,等.基于实体的指挥控制过程仿真建模[J].指挥控制与仿真,2012,34(3):116-120.
[18]张树玲,孙波,田艳琴.过程建模技术研究综述[C]∥2010 Third International Conference on Education Technology and Trai-ning.Wuhan,Hubei,2010:542-547.
[19]PLAIA A,CARRIE A.Application and assessment of IDEF3‐process flow description capture method[J].International Journal of Operations & Production Management,1995,15(1):63-73.
[20]肖磊,金光,周忠宝,等.基于Petri网的维修过程建模仿真方法研究[J].军械工程学院学报,2008,20(5):6-11.
[21]SCHEER A W.Architecture of integrated information system- foundations of enterprise modelling[M].Berlin:Springer-Verlag,1992.

相关文章 15

[1]	饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277
[2]	吴子仪, 李邵梅, 姜梦函, 张建朋. 基于自注意力模型的本体对齐方法 Ontology Alignment Method Based on Self-attention 计算机科学, 2022, 49(9): 215-220. https://doi.org/10.11896/jsjkx.210700190
[3]	孔世明, 冯永, 张嘉云. 融合知识图谱的多层次传承影响力计算与泛化研究 Multi-level Inheritance Influence Calculation and Generalization Based on Knowledge Graph 计算机科学, 2022, 49(9): 221-227. https://doi.org/10.11896/jsjkx.210700144
[4]	徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204
[5]	秦琪琦, 张月琴, 王润泽, 张泽华. 基于知识图谱的层次粒化推荐方法 Hierarchical Granulation Recommendation Method Based on Knowledge Graph 计算机科学, 2022, 49(8): 64-69. https://doi.org/10.11896/jsjkx.210600111
[6]	王杰, 李晓楠, 李冠宇. 基于自适应注意力机制的知识图谱补全算法 Adaptive Attention-based Knowledge Graph Completion 计算机科学, 2022, 49(7): 204-211. https://doi.org/10.11896/jsjkx.210400129
[7]	马瑞新, 李泽阳, 陈志奎, 赵亮. 知识图谱推理研究综述 Review of Reasoning on Knowledge Graph 计算机科学, 2022, 49(6A): 74-85. https://doi.org/10.11896/jsjkx.210100122
[8]	邓凯, 杨频, 李益洲, 杨星, 曾凡瑞, 张振毓. 一种可快速迁移的领域知识图谱构建方法 Fast and Transmissible Domain Knowledge Graph Construction Method 计算机科学, 2022, 49(6A): 100-108. https://doi.org/10.11896/jsjkx.210900018
[9]	杜晓明, 袁清波, 杨帆, 姚奕, 蒋祥. 军事指控保障领域命名实体识别语料库的构建 Construction of Named Entity Recognition Corpus in Field of Military Command and Control Support 计算机科学, 2022, 49(6A): 133-139. https://doi.org/10.11896/jsjkx.210400132
[10]	熊中敏, 舒贵文, 郭怀宇. 融合用户偏好的图神经网络推荐模型 Graph Neural Network Recommendation Model Integrating User Preferences 计算机科学, 2022, 49(6): 165-171. https://doi.org/10.11896/jsjkx.210400276
[11]	钟将, 尹红, 张剑. 基于学术知识图谱的辅助创新技术研究 Academic Knowledge Graph-based Research for Auxiliary Innovation Technology 计算机科学, 2022, 49(5): 194-199. https://doi.org/10.11896/jsjkx.210400195
[12]	朱敏, 梁朝晖, 姚林, 王翔坤, 曹梦琦. 学术引用信息可视化方法综述 Survey of Visualization Methods on Academic Citation Information 计算机科学, 2022, 49(4): 88-99. https://doi.org/10.11896/jsjkx.210300219
[13]	梁静茹, 鄂海红, 宋美娜. 基于属性图模型的领域知识图谱构建方法 Method of Domain Knowledge Graph Construction Based on Property Graph Model 计算机科学, 2022, 49(2): 174-181. https://doi.org/10.11896/jsjkx.210500076
[14]	黄梅根, 刘川, 杜欢, 刘佳乐. 基于知识图谱的认知诊断模型及其在教辅中的应用研究 Research on Cognitive Diagnosis Model Based on Knowledge Graph and Its Application in Teaching Assistant 计算机科学, 2021, 48(6A): 644-648. https://doi.org/10.11896/jsjkx.200700163
[15]	李嘉明, 赵阔, 屈挺, 刘晓翔. 基于知识图谱的区块链物联网领域研究分析 Research and Analysis of Blockchain Internet of Things Based on Knowledge Graph 计算机科学, 2021, 48(6A): 563-567. https://doi.org/10.11896/jsjkx.200600071

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed