Chinese Journal of Intelligent Science and Technology ›› 2022, Vol. 4 ›› Issue (1): 129-141.doi: 10.11959/j.issn.2096-6652.202216
Special Issue: 知识图谱
• Papers and Reports • Previous Articles
Tianhao MOU1, Shaoyuan LI1,2
Revised:
2021-11-25
Online:
2022-03-15
Published:
2022-03-01
Supported by:
CLC Number:
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.
[1] | FERRUCCI D , LEVAS A , BAGCHI S ,et al. Watson:beyond jeopardy![J]. Artificial Intelligence, 2013199/200: 93-105. |
[2] | FENSEL D , ?IM?EK U ,, ANGELE K , et al . Introduction:what is a knowledge graph?[M]// Knowledge graphs.[S.l.:s.n.], 2020: 1-10. |
[3] | 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. |
[4] | 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. |
[5] | 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. |
[6] | 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. |
[7] | MITCHELL T , FREDKIN E . Never-ending language learning[C]// Proceedings of 2014 IEEE International Conference on Big Data. Piscataway:IEEE Press, 2014. |
[8] | 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. |
[9] | 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. |
[10] | 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. |
[11] | 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. |
[12] | 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. | |
[13] | 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. |
[14] | 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. |
[15] | 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. |
[16] | 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. |
[17] | 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. |
[18] | 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. |
[19] | 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. |
[20] | 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. |
[21] | 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. |
[22] | 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. |
[23] | 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. | |
[24] | 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. |
[25] | 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. | |
[26] | 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, 2020143: 107094. |
[27] | 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. |
[28] | 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. |
[29] | 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. |
[30] | GRUBER T R . A translation approach to portable ontology specifications[J]. Knowledge Acquisition, 1993,5(2): 199-220. |
[31] | STUDER R , BENJAMINS V R , FENSEL D . Knowledge engineering:principles and methods[J]. Data & Knowledge Engineering, 1998,25(1/2): 161-197. |
[32] | 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. | |
[33] | 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. | |
[34] | 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. |
[35] | 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. |
[36] | 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. |
[37] | 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. |
[38] | 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. |
[39] | 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. | |
[40] | 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. | |
[41] | 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. |
[42] | 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. |
[43] | IEC. Enterprise-control system integration — Part 1:models and terminology :IEC 62264-1:2013[S]. 2013. |
[44] | SIENIUTYCZ S . Complexity and complex thermo-economic systems[M].[S.l.]: Elsevier, 2019. |
[45] | 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. |
[46] | 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. |
[47] | 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. | |
[48] | IEC. Industrial-process measurement,control and automation-reference model for representation of production facilities (digital factory):GB/Z 32235-2015[S]. 2012. |
[49] | 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. | |
[50] | NOY N , MCGUINNESS D L . Ontology development 101:a guide to creating your first ontology[R]. 2001. |
[51] | DAVIES J , FENSEL D , HARMELEN F V . Towards the Semantic Web:ontology-driven knowledge management[M].[S.l.]: John Wiley &Sons, 2003. |
[52] | 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. |
[53] | 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. |
[54] | CRISTANI M , CUEL R . A survey on ontology creation methodologies[J]. International Journal on Semantic Web and Information Systems, 2005,1(2): 49-69. |
[55] | 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. |
[56] | 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. |
[57] | 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. |
[58] | 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. |
[59] | 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. |
[60] | SPANOS D E , STAVROU P , MITROU N . Bringing relational databases into the Semantic Web:a survey[J]. Semantic Web, 2012,3(2): 169-209. |
[61] | 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. |
[62] | 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. |
[63] | SEQUEDA J F , MIRANKER D P . Ultrawrap:SPARQL execution on relational data[J]. Journal of Web Semantics, 2013,22: 19-39. |
[64] | 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. |
[65] | 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. |
[66] | BORTHWICK A E . A maximum entropy approach to named entity recognition[D]. New York:New York University, 1999. |
[67] | 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. |
[68] | 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. |
[69] | 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. |
[70] | 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. |
[71] | 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. |
[72] | 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. |
[73] | 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. |
[74] | 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. | |
[75] | 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. | |
[76] | 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. |
[77] | 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. |
[78] | 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. |
[79] | 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. |
[80] | 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. |
[81] | 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. |
[82] | 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. |
[83] | 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. |
[84] | 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. |
[85] | 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. |
[86] | 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. |
[87] | 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. |
[88] | 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. |
[89] | 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. |
[90] | 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. |
[91] | 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. |
[92] | 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. |
[93] | 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. |
[94] | 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. |
[95] | 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. |
[96] | 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). |
[97] | 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. |
[98] | IORDANOV B , . HyperGraphDB:a generalized graph database[C]// Proceedings of the Web-Age Information Management.[S.l.:s.n.], 2010. |
[99] | 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. | |
[100] | 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. |
[101] | MARQUARDT W , MORBACH J , WIESNER A ,et al. OntoCAPE:a re-usable ontology for chemical process engineering[M]. Heidelberg: Springer, 2010. |
[102] | MARQUARDT W , MORBACH J , WIESNER A ,et al. Chemical process systems[M]. Heidelberg: Springer, 2009: 241-321. |
[103] | ZHOU L N . Ontology learning:state of the art and open issues[J]. Information Technology and Management, 2007,8(3): 241-252. |
[104] | LIU G , GUO J B . Bidirectional LSTM with attention mechanism and convolutional layer for text classification[J]. Neurocomputing, 2019,337: 325-338. |
[105] | 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. |
[106] | 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. |
[107] | DOWNS J J , VOGEL E F . A plant-wide industrial process control problem[J]. Computers & Chemical Engineering, 1993,17(3): 245-255. |
[108] | 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. |
[109] | 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. |
[110] | 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. |
[111] | FARAZI F , SALAMANCA M , MOSBACH S ,et al. Knowledge graph approach to combustion chemistry and interoperability[J]. ACS Omega, 2020,5(29): 18342-18348. |
[112] | 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. | |
[113] | 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. |
[114] | 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. |
[1] | Yidong LI, Zikai ZHANG, Hairong DONG, Honglei ZHANG, Haoyu CHEN, Yushan HAN. Information security of the industrial control system for rail:analysis and prospect [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(2): 139-148. |
[2] | Junhao LIN,Dongqin FENG. Process stability modeling and attack protection of industrial control system based on reverse cloud algorithm model [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(1): 53-61. |
[3] | Guolian HOU,Linjuan GONG,Ye SU,Yaohua TANG. ACP based parallel power generation control system [J]. Chinese Journal of Intelligent Science and Technology, 2019, 1(3): 269-279. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
|