流程工业控制系统的知识图谱构建

牟天昊; 李少远

doi:10.11959/j.issn.2096-6652.202216

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流程工业控制系统的知识图谱构建

学术论文 | 更新时间：2024-06-05

- 流程工业控制系统的知识图谱构建
- Knowledge graph construction for control systems in process industry
- 智能科学与技术学报 2022年4卷第1期页码：129-141
- 作者机构：
  
  1. 上海交通大学电子信息与电气工程学院，上海 200240
  2. 上海交通大学系统控制与信息处理教育部重点实验室，上海 200240
- 作者简介：
  
  [ "牟天昊（1998− ），男，上海交通大学电子信息与电气工程学院博士生，主要研究方向为知识图谱、机器学习以及它们在流程工业中的应用" ]
  [ "李少远（1965− ），男，博士，上海交通大学电子信息与电气工程学院讲席教授，国家杰出青年科学基金获得者，科技创新 2030—“新一代人工智能”重大项目负责人。主要研究方向为预测控制、工业智能、满意优化控制、分布式优化等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2018AAA0101701)
- DOI：10.11959/j.issn.2096-6652.202216
  中图分类号： TP182
- 网络出版日期：2022-03，
  
  纸质出版日期：2022-03-15
- 稿件说明：
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牟天昊, 李少远. 流程工业控制系统的知识图谱构建[J]. 智能科学与技术学报, 2022,4(1):129-141.

Tianhao MOU, Shaoyuan LI. Knowledge graph construction for control systems in process industry[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 129-141.
牟天昊, 李少远. 流程工业控制系统的知识图谱构建[J]. 智能科学与技术学报, 2022,4(1):129-141. DOI： 10.11959/j.issn.2096-6652.202216.

Tianhao MOU, Shaoyuan LI. Knowledge graph construction for control systems in process industry[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 129-141. DOI： 10.11959/j.issn.2096-6652.202216.

摘要

近年来，工业控制系统智能化的趋势方兴未艾，相关新技术新思想不断被提出。知识图谱是人工智能应用的基础资源，构建专业领域知识图谱已经成为研究热点。然而，控制系统知识图谱构建尚处于发展初期。对控制系统的结构特点和任务要求进行分析，给出控制系统的知识图谱构建的方法框架。首先对已有的流程工业控制系统知识图谱构建的工作进行总结，阐述了工业控制系统的特点，给出了控制系统知识图谱构建的基本原则和流程，并以控制系统信息物理资产管理任务为例进行了详细的知识图谱构建说明。最后，对未来的研究方向进行了展望。

Abstract

Achieving intelligence in industrial control systems is a prevailing trend in recent years

with numerous new technologies and ideas prompted.Knowledge graph is a fundamental resource for artificial intelligence

and domain-specific knowledge graph construction attracts a lot of research attentions.However

knowledge graph construction for control systems is still in the early stage of exploitation.In this paper

structural characteristics and task requirements of process control systems were analyzed.Furthermore

a knowledge graph construction methodology architecture for process control systems was proposed.Firstly

a brief summary on existing related works was given.After that

the characteristics of process industry control systems were analyzed

and the corresponding knowledge graph construction principles and procedures were proposed.Cyber-physical assets management was taken as a case study for detailed explanation.Finally

a prospect for the future research directions of knowledge graph construction for process control systems was made.

关键词

Keywords

references

FERRUCCI D , LEVAS A , BAGCHI S , et al . Watson:beyond jeopardy! [J ] . Artificial Intelligence , 2013 199/200 : 93 - 105 .

FENSEL D , ŞIMŞEK U ,, ANGELE K , et al . Introduction:what is a knowledge graph? [M ] // Knowledge graphs .[S.l.:s.n. ] , 2020 : 1 - 10 .

WANG Q , MAO Z D , WANG B , et al . Knowledge graph embedding:a survey of approaches and applications [J ] . IEEE Transactions on Knowledge and Data Engineering , 2017 , 29 ( 12 ): 2724 - 2743 .

LEHMANN J , ISELE R , JAKOB M , et al . DBpedia-a large-scale,multilingual knowledge base extracted from Wikipedia [J ] . Semantic Web , 2015 , 6 ( 2 ): 167 - 195 .

BOLLACKER K , EVANS C , PARITOSH P , et al . Freebase:a collaboratively created graph database for structuring human knowledge [C ] // Proceedings of 2008 ACM SIGMOD International Conference on Management of Data . New York:ACM Press , 2008 : 1247 - 1250 .

SUCHANEK F M , KASNECI G , WEIKUM G . YAGO:a core of semantic knowledge [C ] // Proceedings of the 16th international conference on World Wide Web .[S.l.:s.n. ] , 2007 : 697 - 706 .

MITCHELL T , FREDKIN E . Never-ending language learning [C ] // Proceedings of 2014 IEEE International Conference on Big Data . Piscataway:IEEE Press , 2014 .

ROTMENSCH M , HALPERN Y , TLIMAT A , et al . Learning a health knowledge graph from electronic medical records [J ] . Scientific Reports , 2017 , 7 ( 1 ): 1 - 11 .

ZHANG Y , SHENG M , ZHOU R , et al . HKGB:an inclusive,extensible,intelligent,semi-auto-constructed knowledge graph framework for healthcare with clinicians’ expertise incorporated [J ] . Information Processing ＆ Management , 2020 , 57 ( 6 ): 102324 .

MA F L , YOU Q Z , XIAO H P , et al . KAME:knowledge-based attention model for diagnosis prediction in healthcare [C ] // Proceedings of the 27th ACM International Conference on Information and Knowledge Management . New York:ACM Press , 2018 : 743 - 752 .

CUI L M , SEO H , TABAR M , et al . Deterrent:knowledge guided graph attention network for detecting healthcare misinformation [C ] // Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining . New York:ACM Press , 2020 : 492 - 502 .

谭玲 , 鄂海红 , 匡泽民 , 等 . 医学知识图谱构建关键技术及研究进展 [J ] . 大数据 , 2021 , 7 ( 4 ): 80 - 104 .

TAN L , E H H , KUANG Z M , et al . Key technologies and research progress of medical knowledge graph construction [J ] . Big Data Research , 2021 , 7 ( 4 ): 80 - 104 .

CHEN P H , LU Y , ZHENG V W , et al . KnowEdu:a system to construct knowledge graph for education [J ] . IEEE Access , 2018 , 6 : 31553 - 31563 .

SHI D Q , WANG T , XING H , et al . A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning [J ] . Knowledge-Based Systems , 2020 , 195 : 105618 .

CHI Y , QIN Y , SONG R , et al . Knowledge graph in smart education:a case study of entrepreneurship scientific publication management [J ] . Sustainability , 2018 , 10 ( 4 ): 995 .

LIU Y , ZENG Q G , ORDIERES MERÉ J , et al . Anticipating stock market of the renowned companies:a knowledge graph approach [J ] . Complexity , 2019 :9202457.

FU X Y , REN X Q , MENGSHOEL O J , et al . Stochastic optimization for market return prediction using financial knowledge graph [C ] // Proceedings of 2018 IEEE International Conference on Big Knowledge . Piscataway:IEEE Press , 2018 : 25 - 32 .

DING X , ZHANG Y , LIU T , et al . Knowledge-driven event embedding for stock prediction [C ] // Proceedings of the 26th International Conference on Computational .[S.l.:s.n. ] , 2016 : 2133 - 2142 .

WANG C , ZHU H Y . Representing fine-grained co-occurrences for behavior-based fraud detection in online payment services [J ] . IEEE Transactions on Dependable and Secure Computing , 2020 , 19 ( 1 ): 301 - 315 .

CHEN W , ZHANG X , WANG T J , et al . Opinion-aware knowledge graph for political ideology detection [C ] // Proceedings of the 26th International Joint Conference on Artificial Intelligence . California:International Joint Conferences on Artificial Intelligence Organization , 2017 : 3647 - 3653 .

LIU S , YANG H , LI J Y , et al . Preliminary study on the knowledge graph construction of Chinese ancient history and culture [J ] . Information , 2020 , 11 ( 4 ): 186 .

FAN T , WANG H . Research of Chinese intangible cultural heritage knowledge graph construction and attribute value extraction with graph attention network [J ] . Information Processing ＆ Management , 2022 , 59 ( 1 ): 102753 .

李望月 , 刘瑾 , 陈娜 . 大数据技术在乡村画像中的应用研究 [J ] . 大数据 , 2020 , 6 ( 1 ): 99 - 118 .

LI W Y , LIU J , CHEN N . Application research of big data technology in rural portrait [J ] . Big Data Research , 2020 , 6 ( 1 ): 99 - 118 .

LI X Y , LYU M T , WANG Z X , et al . Exploiting knowledge graphs in industrial products and services:a survey of key aspects,challenges,and future perspectives [J ] . Computers in Industry , 2021 , 129 : 103449 .

王文广 . 知识图谱推理:现代的方法与应用 [J ] . 大数据 , 2021 , 7 ( 3 ): 42 - 59 .

WANG W G . Knowledge graph reasoning:modern methods and applications [J ] . Big Data Research , 2021 , 7 ( 3 ): 42 - 59 .

MAO S , ZHAO Y M , CHEN J H , et al . Development of process safety knowledge graph:a case study on delayed coking process [J ] . Computers ＆ Chemical Engineering , 2020 143 : 107094 .

ZHOU L , PAN M , SIKORSKI J J , et al . Towards an ontological infrastructure for chemical process simulation and optimization in the context of eco-industrial parks [J ] . Applied Energy , 2017 , 204 : 1284 - 1298 .

CHEN Z Y , LIU Y , VALERA-MEDINA A , , et al . Multi-sourced modelling for strip breakage using knowledge graph embeddings [J ] . Procedia CIRP , 2021 , 104 : 1884 - 1889 .

SHEN G W , WANG W L , MU Q L , et al . Data-driven cybersecurity knowledge graph construction for industrial control system security [J ] . Wireless Communications and Mobile Computing , 2020 :8883696.

GRUBER T R . A translation approach to portable ontology specifications [J ] . Knowledge Acquisition , 1993 , 5 ( 2 ): 199 - 220 .

STUDER R , BENJAMINS V R , FENSEL D . Knowledge engineering:principles and methods [J ] . Data ＆ Knowledge Engineering , 1998 , 25 ( 1/2 ): 161 - 197 .

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 .

XU Z L , SHENG Y P , HE L R , et al . Review on knowledge graph techniques [J ] . Journal of University of Electronic Science and Technology of China , 2016 , 45 ( 4 ): 589 - 606 .

刘峤 , 李杨 , 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 .

LIU Q , LI Y , DUAN H , et al . Knowledge graph construction techniques [J ] . Journal of Computer Research and Development , 2016 , 53 ( 3 ): 582 - 600 .

ZHAO Z , HAN S K , SO I M . Architecture of knowledge graph construction techniques [J ] . International Journal of Pure and Applied Mathematics , 2018 , 118 ( 19 ): 1869 - 1883 .

FU L J , YV B , ZHONG Z Y . Constructing a vertical knowledge graph for non-traditional machining industry [C ] // Proceedings of 2018 IEEE 15th International Conference on Networking,Sensing and Control . Piscataway:IEEE Press , 2018 : 1 - 5 .

LIANG H , PENG X J , ZHAO N N , et al . An approach of top-down electric generation knowledge graph construction [J ] . IOP Conference Series:Earth and Environmental Science , 2021 , 661 ( 1 ): 012021 .

KOU C , LIU T , MA L , et al . Construction and application research of knowledge graph in spacecraft launch [J ] . Journal of Physics:Conference Series , 2021 , 1754 ( 1 ): 012180 .

JIA Y , QI Y , SHANG H , et al . A practical approach to constructing a knowledge graph for cybersecurity [J ] . Engineering , 2018 , 4 ( 1 ): 53 - 60 .

柴洪峰 , 王帅 , 涂晓军 , 等 . 智能化金融科技创新监管工具:理念、平台框架和展望 [J ] . 智能科学与技术学报 , 2020 , 2 ( 3 ): 214 - 226 .

CHAI H F , WANG S , TU X J , et al . Intelligent innovative regulatory tools on financial technology:concept,platform framework,and prospects [J ] . Chinese Journal of Intelligent Science and Technology , 2020 , 2 ( 3 ): 214 - 226 .

陈强 , 代仕娅 . 基于金融知识图谱的会计欺诈风险识别方法 [J ] . 大数据 , 2021 , 7 ( 3 ): 116 - 129 .

CHEN Q , DAI S Y . Recognition method of accounting fraud risk based on financial knowledge graph [J ] . Big Data Research , 2021 , 7 ( 3 ): 116 - 129 .

MEILICKE C , CHEKOL M W , RUFFINELLI D , et al . Anytime bottom-up rule learning for knowledge graph completion [C ] // Proceedings of the 28th International Joint Conference on Artificial Intelligence . California:International Joint Conferences on Artificial Intelligence Organization , 2019 : 3137 - 3143 .

ZHENG X , WANG B , ZHAO Y M , et al . A knowledge graph method for hazardous chemical management:ontology design and entity identification [J ] . Neurocomputing , 2021 , 430 : 104 - 111 .

IEC . Enterprise-control system integration — Part 1:models and terminology :IEC 62264-1:2013 [S ] . 2013 .

SIENIUTYCZ S . Complexity and complex thermo-economic systems [M ] .[S.l. ] : Elsevier , 2019 .

RAJKUMAR R , LEE I , SHA L , et al . Cyber-physical systems:the next computing revolution [C ] // Proceedings of Design Automation Conference . Piscataway:IEEE Press , 2010 : 731 - 736 .

QIAN F , ZHONG W M , DU W L . Fundamental theories and key technologies for smart and optimal manufacturing in the process industry [J ] . Engineering , 2017 , 3 ( 2 ): 14 - 27 .

袁小锋 , 王雅琳 , 阳春华 , 等 . 深度学习在流程工业过程数据建模中的应用 [J ] . 智能科学与技术学报 , 2020 , 2 ( 2 ): 107 - 115 .

YUAN X F , WANG Y L , YANG C H , et al . The application of deep learning in data-driven modeling of process industries [J ] . Chinese Journal of Intelligent Science and Technology , 2020 , 2 ( 2 ): 107 - 115 .

IEC . Industrial-process measurement,control and automation-reference model for representation of production facilities (digital factory):GB/Z 32235-2015 [S ] . 2012 .

黄德先 , 江永亨 , 金以慧 . 炼油工业过程控制的研究现状、问题与展望 [J ] . 自动化学报 , 2017 , 43 ( 6 ): 902 - 916 .

HUANG D X , JIANG Y H , JIN Y H . Present research situation,major bottlenecks,and prospect of refinery industry process control [J ] . Acta Automatica Sinica , 2017 , 43 ( 6 ): 902 - 916 .

NOY N , MCGUINNESS D L . Ontology development 101:a guide to creating your first ontology [R ] . 2001 .

DAVIES J , FENSEL D , HARMELEN F V . Towards the Semantic Web:ontology-driven knowledge management [M ] .[S.l. ] : John Wiley ＆Sons , 2003 .

GRNINGER M , FOX M S . Methodology for the design and evaluation of ontologies [C ] // Proceedings of the 14th International Joint Conference on Artificial Intelligence .[S.l.:s.n. ] , 1995 .

LOPEZ M F , GOMEZ-PEREZ A ,, SIERRA J P , et al . Building a chemical ontology using Methontology and the ontology design environment [J ] . IEEE Intelligent Systems and Their Applications , 1999 , 14 ( 1 ): 37 - 46 .

CRISTANI M , CUEL R . A survey on ontology creation methodologies [J ] . International Journal on Semantic Web and Information Systems , 2005 , 1 ( 2 ): 49 - 69 .

NOY N F , FERGERSON R W , MUSEN M A . The knowledge model of Protégé-2000:combining interoperability and flexibility [C ] // Proceedings of the Knowledge Engineering and Knowledge Management Methods,Models,and Tools .[S.l.:s.n. ] , 2000 .

BECHHOFER S , HORROCKS I , GOBLE C , et al . OilEd:a reason-able ontology editor for the semantic web [C ] // Proceedings of 2001 Annual Conference on Artificial Intelligence .[S.l.:s.n. ] , 2001 : 396 - 408 .

ARPÍREZ J C , CORCHO O , FERNÁNDEZ-LÓPEZ M , et al . WebODE:a scalable workbench for ontological engineering [C ] // Proceedings of the 1st international conference on Knowledge Capture .[S.l.:s.n. ] , 2001 : 6 - 13 .

SURE Y , ERDMANN M , ANGELE J , et al . OntoEdit:collaborative ontology development for the semantic web [C ] // Proceedings of 2002 International Semantic Web Conference .[S.l.:s.n. ] , 2002 : 221 - 235 .

HERT M , REIF G , GALL H C . A comparison of RDB-to-RDF mapping languages [C ] // Proceedings of the 7th International Conference on Semantic Systems .[S.l.:s.n. ] , 2011 : 25 - 32 .

SPANOS D E , STAVROU P , MITROU N . Bringing relational databases into the Semantic Web:a survey [J ] . Semantic Web , 2012 , 3 ( 2 ): 169 - 209 .

BIZER C , SEABORNE A . D2RQ-treating non-RDF databases as virtual RDF graphs [C ] // Proceedings of the 3rd International Semantic Web Conference .[S.l.:s.n. ] , 2004 .

CALVANESE D , DE GIACOMO G , LEMBO D , et al . The MASTRO system for ontology-based data access [J ] . Semantic Web , 2011 , 2 ( 1 ): 43 - 53 .

SEQUEDA J F , MIRANKER D P . Ultrawrap:SPARQL execution on relational data [J ] . Journal of Web Semantics , 2013 , 22 : 19 - 39 .

PRIYATNA F , CORCHO O , SEQUEDA J . Formalisation and experiences of R2RML-based SPARQL to SQL query translation using morph [C ] // Proceedings of the 23rd international conference on World wide web . 2014 : 479 - 490 .

MCCALLUM A , LI W . Early results for named entity recognition with conditional random fields,feature induction and web-enhanced lexicons [C ] // Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL 2003.Morristown:Association for Computational Linguistics . 2003 : 188 - 191 .

BORTHWICK A E . A maximum entropy approach to named entity recognition [D ] . New York:New York University , 1999 .

MIKHEEV A , GROVER C , MOENS M . Description of the LTG system used for MUC-7 [C ] // Proceedings of the 7th Message Understanding Conference .[S.l.:s.n. ] , 1998 .

BIKEL D M , MILLER S , SCHWARTZ R , et al . Nymble:a high-performance learning name-finder [C ] // Proceedings of the 5th Conference on Applied Natural Language Processing . Morristin:Association for Computational Linguistics , 1997 : 194 - 201 .

SUN J , GAO J F , ZHANG L , et al . Chinese named entity identification using class-based language model [C ] // Proceedings of the 19th International Conference on Computational Linguistics . Morristown:Association for Computational Linguistics , 2002 .

ZHANG H P , LIU Q , YU H K , et al . Chinese named entity recognition using role model [J ] . International Journal of Computational Linguistics ＆ Chinese Language Processing . 2003 , 8 ( 2 ): 29 - 60 .

DOWNEY D , BROADHEAD M , ETZIONI O . Locating complex named entities in web text [C ] // Proceedings of the 20th International Joint Conferences on Artificial Intelligence .[S.l.:s.n. ] , 2007 : 2733 - 2739 .

SEKINE S , GRISHMAN R , SHINNOU H . A decision tree method for finding and classifying names in Japanese texts [C ] // Proceedings of the 6th Workshop On Very Large Corpora .[S.l.:s.n. ] , 1998 .

LAMPLE G , BALLESTEROS M , SUBRAMANIAN S , et al . Neural architectures for named entity recognition [C ] // Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies . Stroudsburg:Association for Computational Linguistics , 2016 .

胡志磊 , 靳小龙 , 陈剑赟 , 等 . 事件图谱的构建、推理与应用 [J ] . 大数据 , 2021 , 7 ( 3 ): 80 - 96 .

HU Z L , JIN X L , CHEN J Y , et al . Construction,reasoning and applications of event graphs [J ] . Big Data Research , 2021 , 7 ( 3 ): 80 - 96 .

赵军 , 刘康 , 何世柱 , 等 . 知识图谱 [M ] . 北京 : 高等教育出版社 , 2018 .

ZHAO J , LIN K , HE S Z , et al . Knowledge graph [M ] . Beijing : Higher Education Press , 2018 .

ZELENKO D , AONE C , RICHARDELLA A . Kernel methods for relation extraction [C ] // Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing . Morristown:Association for Computational Linguistics , 2002 .

BUNESCU R C , MOONEY R J . A shortest path dependency kernel for relation extraction [C ] // Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing . Morristown:Association for Computational Linguistics , 2005 : 724 - 731 .

ZHANG M , ZHANG J , SU J , et al . A composite kernel to extract relations between entities with both flat and structured features [C ] // Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL . Morristown:Association for Computational Linguistics , 2006 : 825 - 832 .

ZENG D , LIU K , LAI S , et al . Relation classification via convolutional deep neural network [C ] // Proceedings of the 25th International Conference on Computational Linguistics:Technical Papers .[S.l.:s.n. ] , 2014 : 2335 - 2344 .

WANG L L , CAO Z , DE MELO G , et al . Relation classification via multi-level attention CNNs [C ] // Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics . Stroudsburg:Association for Computational Linguistics , 2016 : 1298 - 1307 .

SOCHER R , HUVAL B , MANNING C D , et al . Semantic compositionality through recursive matrix-vector spaces [C ] // Proceedings of 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning .[S.l.:s.n. ] , 2012 : 1201 - 1211 .

MINTZ M , BILLS S , SNOW R , et al . Distant supervision for relation extraction without labeled data [C ] // Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP:Volume 2 . Morristown:Association for Computational Linguistics , 2009 : 1003 - 1011 .

RIEDEL S , YAO L M , MCCALLUM A . Modeling relations and their mentions without labeled text [C ] // Proceedings of the Machine Learning and Knowledge Discovery in Databases .[S.l.:s.n. ] , 2010 : 148 - 163 .

NGUYEN T V T , MOSCHITTI A . End-to-end relation extraction using distant supervision from external semantic repositories [C ] // Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies .[S.l.:s.n. ] , 2011 : 277 - 282 .

BAGGA A , BALDWIN B . Entity-based cross-document coreferencing using the vector space model [C ] // Proceedings of the 36th Annual Meeting on Association for Computational Linguistics . Morristown:Association for Computational Linguistics , 1998 : 79 - 85 .

MANN G S , YAROWSKY D . Unsupervised personal name disambiguation [C ] // Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL 2003 . Morristown:Association for Computational Linguistics , 2003 : 33 - 40 .

MALIN B , . Unsupervised name disambiguation via social network similarity [C ] // Proceedings of the Workshop on Link Analysis,Counterterrorism,and Security .[S.l.:s.n. ] , 2005 : 93 - 102 .

MEDELYAN O , WITTEN I H , MILNE D . Topic indexing with Wikipedia [C ] // Proceedings of the AAAI 2008 Workshop on Wikipedia and Artificial Intelligence .[S.l.:s.n. ] , 2008 : 19 - 24 .

HAN X P , SUN L , ZHAO J . Collective entity linking in web text:a graph-based method [C ] // Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 2011 : 765 - 774 .

WEN P M , YE Z W , DING W J , et al . A review of research progress on the disambiguation of named entities [J ] . Data Analysis and Knowledge Discovery , 2020 .

TAKANOBU R , ZHANG T Y , LIU J X , et al . A hierarchical framework for relation extraction with reinforcement learning [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2019 , 33 : 7072 - 7079 .

FADER A , SODERLAND S , ETZIONI O , . Identifying relations for open information extraction [C ] // Proceedings of the Conference on Empirical Methods in Natural Language Processing .[S.l.:s.n. ] , 2011 : 1535 - 1545 .

WILKINSON K , SAYERS C , KUNO H , et al . Efficient RDF storage and retrieval in Jena2 [C ] // Proceedings of the 1st International Conference on Semantic Web and Databases .[S.l.:s.n. ] , 2003 : 120 - 139 .

CHONG E I , DAS S , EADON G , et al . An efficient SQL-based RDF querying scheme [C ] // Proceedings of the 31st International Conference on Very Large Data Bases .[S.l.:s.n. ] , 2005 : 1216 - 1227 .

HARRIS S W , GIBBINS N . 3store:efficient bulk RDF storage [C ] // Proceedings of the 1st International Workshop on Practical and Scalable Semantic Systems .[S.l.:s.n. ] , 2003 : 1 - 15 .

MILLER J J , . Graph database applications and concepts with Neo4j [C ] // Proceedings of the Southern Association For Information Systems Conference .[S.l.:s.n ] , 2013 ,2324(36).

FERNANDES D , BERNARDINO J . Graph databases comparison:AllegroGraph,ArangoDB,InfiniteGraph,Neo4J,and OrientDB [C ] // Proceedings of the 7th International Conference on Data Science,Technology and Applications .[S.l.:s.n. ] , 2018 : 373 - 380 .

IORDANOV B , . HyperGraphDB:a generalized graph database [C ] // Proceedings of the Web-Age Information Management .[S.l.:s.n. ] , 2010 .

刘宝珠 , 王鑫 , 柳鹏凯 , 等 . KGDB:统一模型和语言的知识图谱数据库管理系统 [J ] . 软件学报 , 2021 , 32 ( 3 ): 781 - 804 .

LIU B Z , WANG X , LIU P K , et al . KGDB:knowledge graph database system with unified model and query language [J ] . Journal of Software , 2021 , 32 ( 3 ): 781 - 804 .

MORBACH J , YANG A D , MARQUARDT W . OntoCAPE—a large-scale ontology for chemical process engineering [J ] . Engineering Applications of Artificial Intelligence , 2007 , 20 ( 2 ): 147 - 161 .

MARQUARDT W , MORBACH J , WIESNER A , et al . OntoCAPE:a re-usable ontology for chemical process engineering [M ] . Heidelberg : Springer , 2010 .

MARQUARDT W , MORBACH J , WIESNER A , et al . Chemical process systems [M ] . Heidelberg : Springer , 2009 : 241 - 321 .

ZHOU L N . Ontology learning:state of the art and open issues [J ] . Information Technology and Management , 2007 , 8 ( 3 ): 241 - 252 .

LIU G , GUO J B . Bidirectional LSTM with attention mechanism and convolutional layer for text classification [J ] . Neurocomputing , 2019 , 337 : 325 - 338 .

MALIN B , AIROLDI E , CARLEY K M . A network analysis model for disambiguation of names in lists [J ] . Computational ＆ Mathematical Organization Theory , 2005 , 11 ( 2 ): 119 - 139 .

LIU A L , SODERLAND S , BRAGG J , et al . Effective crowd annotation for relation extraction [C ] // Proceedings of 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Stroudsburg:Association for Computational Linguistics . 2016 : 897 - 906 .

DOWNS J J , VOGEL E F . A plant-wide industrial process control problem [J ] . Computers ＆ Chemical Engineering , 1993 , 17 ( 3 ): 245 - 255 .

WEBBER J , . A programmatic introduction to Neo4j [C ] // Proceedings of the 3rd Annual Conference on Systems,Programming,and Applications:Software for Humanity .[S.l.:s.n. ] , 2012 : 217 - 218 .

ZHOU X C , LIM M Q , KRAFT M . A smart contract-based agent marketplace for the J-Park Simulator - a knowledge graph for the process industry [J ] . Computers ＆ Chemical Engineering , 2020 ,139:106896.

ZHOU X C , EIBECK A , LIM M Q , et al . An agent composition framework for the J-Park Simulator - a knowledge graph for the process industry [J ] . Computers ＆ Chemical Engineering , 2019 ,130:106577.

FARAZI F , SALAMANCA M , MOSBACH S , et al . Knowledge graph approach to combustion chemistry and interoperability [J ] . ACS Omega , 2020 , 5 ( 29 ): 18342 - 18348 .

王飞跃 . 平行控制与数字孪生:经典控制理论的回顾与重铸 [J ] . 智能科学与技术学报 , 2020 , 2 ( 3 ): 293 - 300 .

WANG F Y . Parallel control and digital twins:control theory revisited and reshaped [J ] . Chinese Journal of Intelligent Science and Technology , 2020 , 2 ( 3 ): 293 - 300 .

EIBECK A , LIM M Q , KRAFT M . J-Park Simulator:an ontology-based platform for cross-domain scenarios in process industry [J ] . Computers ＆ Chemical Engineering , 2019 ,131:106586.

WU D Y , ZHAO J S . Process topology convolutional network model for chemical process fault diagnosis [J ] . Process Safety and Environmental Protection , 2021 , 150 : 93 - 109 .

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