智能科学与技术学报 ›› 2023, Vol. 5 ›› Issue (1): 69-82.doi: 10.11959/j.issn.2096-6652.202254
王晓1,2,3, 杨林瑶2,4, 胡斌2,3, 侯家琛5
修回日期:
2022-10-13
出版日期:
2023-03-15
发布日期:
2023-03-01
作者简介:
王晓(1988– ),女,博士,安徽大学人工智能学院教授,主要研究方向为社会交通、动态网群组织、人工智能和社会网络分析基金资助:
Xiao WANG1,2,3, Linyao YANG2,4, Bin HU2,3, Jiachen HOU5
Revised:
2022-10-13
Online:
2023-03-15
Published:
2023-03-01
Supported by:
摘要:
知识图谱基于结构化三元组描述事实知识,能够有效刻画现实世界实体间的语义关系,已成为新一代人工智能的关键共性技术。总结了多源知识图谱的发展概况、典型应用以及知识协同存在的问题,提出了一种基于ACP方法的多源知识图谱知识协同框架——平行推理,该框架基于人工系统、计算实验和平行执行实现了多源知识的抽取、融合补全及无偏化应用。以电网低压减载场景为例,基于仿真试验验证了平行推理对解决复杂系统管控问题的有效性。
中图分类号:
王晓, 杨林瑶, 胡斌, 等. 平行推理:一种基于ACP方法的虚实互动的知识协同框架[J]. 智能科学与技术学报, 2023, 5(1): 69-82.
Xiao WANG, Linyao YANG, Bin HU, et al. Parallel reasoning: a virtual-real interactive knowledge collaboration framework based on ACP approach[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 69-82.
[1] | BERNERS-LEE T , HENDLER J , LASSILA O . The semantic web[J]. Scientific American, 2001,284(5): 34-43. |
[2] | 王飞跃, 张俊 . 智联网:概念、问题和平台[J]. 自动化学报, 2017,43(12): 2061-2070. |
WANG F Y , ZHANG J . Internet of minds:the concept,issues and platforms[J]. Acta Automatica Sinica, 2017,43(12): 2061-2070. | |
[3] | 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. |
MOU T H , LI S Y . Knowledge graph construction for control systems in process industry[J]. Chinese Journal of Intelligent Science and Technology, 2022,4(1): 129-141. | |
[4] | CHEN X J . A review:knowledge reasoning over knowledge graph[J]. Expert Systems With Applications, 2020,141. |
[5] | 陈名杨, 张文, 陈湘楠 ,等. 群体知识图谱:分布式知识迁移与联邦式图谱推理[J]. 智能科学与技术学报, 2022,4(1): 55-64. |
CHEN M Y , ZHANG W , CHEN X N ,et al. Collective knowledge graph:meta knowledge transfer and federated graph reasoning[J]. Chinese Journal of Intelligent Science and Technology, 2022,4(1): 55-64. | |
[6] | CHEN X L , CHEN M H , FAN C J ,et al. Multilingual knowledge graph completion via ensemble knowledge transfer[C]// Proceedings of Findings of the Association for Computational Linguistics:EMNLP 2020. Stroudsburg:Association for Computational Linguistics, 2020: 3227-3238. |
[7] | 王飞跃 . 人工社会、计算实验、平行系统:关于复杂社会经济系统计算研究的讨论[J]. 复杂系统与复杂性科学, 2004,1(4): 25-35. |
WANG F Y . Artificial societies,computational experiments,and parallel systems:a discussion on computational theory of complex social-economic systems[J]. Complex Systems and Complexity Science, 2004,1(4): 25-35. | |
[8] | WANG S Y , HOUSDEN J , BAI T X ,et al. Robotic intra-operative ultrasound:virtual environments and parallel systems[J]. IEEE/CAA Journal of Automatica Sinica, 2021,8(5): 1095-1106. |
[9] | 王飞跃, 孟祥冰, 杜思聪 ,等. 平行光场:基本框架与流程[J]. 智能科学与技术学报, 2021,3(1): 110-122. |
WANG F Y , MENG X B , DU S C ,et al. Parallel light field:the framework and processes[J]. Chinese Journal of Intelligent Science and Technology, 2021,3(1): 110-122. | |
[10] | LU J W , WEI Q L , WANG F Y . Parallel control for optimal tracking via adaptive dynamic programming[J]. IEEE/CAA Journal of Automatica Sinica, 2020,7(6): 1662-1674. |
[11] | 王春法, 王飞跃, 鲁越 ,等. 平行博物馆:新时代博物馆运营的智能管理与控制[J]. 智能科学与技术学报, 2021,3(2): 125-136. |
WANG C F , WANG F Y , LU Y ,et al. Parallel museums:intelligent management and control of museum operations in the new era[J]. Chinese Journal of Intelligent Science and Technology, 2021,3(2): 125-136. | |
[12] | WEI Q L , LI H Y , WANG F Y . Parallel control for continuous-time linear systems:a case study[J]. IEEE/CAA Journal of Automatica Sinica, 2020,7(4): 919-928. |
[13] | 王飞跃 . 平行医学:从医学的温度到智慧的医学[J]. 智能科学与技术学报, 2021,3(1): 1-9. |
WANG F Y . Parallel medicine:from warmness of medicare to medicine of smartness[J]. Chinese Journal of Intelligent Science and Technology, 2021,3(1): 1-9. | |
[14] | ZHAO X , ZENG W X , TANG J Y ,et al. An experimental study of state-of-the-art entity alignment approaches[J]. IEEE Transactions on Knowledge and Data Engineering, 2022,34(6): 2610-2625. |
[15] | RICHENS R H . Report on research:Cambridge language research unit[J]. Mechanical Translation, 1956,3: 36-37. |
[16] | GRUBER T R . A translation approach to portable ontology specifications[J]. Knowledge Acquisition, 1993,5(2): 199-220. |
[17] | BERNERS-LEE T , HENDLER J . Publishing on the semantic web[J]. Nature, 2001,410(6832): 1023-1024. |
[18] | BIZER C , HEATH T , IDEHEN K ,et al. Linked data on the web (LDOW2008)[C]// Proceedings of the 17th International Conference on World Wide Web.[S.l.:s.n.], 2008: 1265-1266. |
[19] | 肖仰华, 徐波, 林欣 . 知识图谱:概念与技术[M]. 北京: 电子工业出版社, 2020. |
XIAO Y H , XU B , LIN X . Knowledge graph:concepts and technologies[M]. Beijing: Publishing House of Electronics Industry, 2020. | |
[20] | ZHUANG Y , LI G L , ZHONG Z J ,et al. Hike:a hybrid human-machine method for entity alignment in large-scale knowledge bases[C]// Proceedings of 2017 ACM Conference on Information and Knowledge Management. New York:ACM Press, 2017: 1917-1926. |
[21] | CHEN M H , TIAN Y T , YANG M H ,et al. Multilingual knowledge graph embeddings for cross-lingual knowledge alignment[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. California:International Joint Conferences on Artificial Intelligence Organization, 2017: 1511-1517. |
[22] | LI C J , CAO Y X , HOU L ,et al. Semi-supervised entity alignment via joint knowledge embedding model and cross-graph model[C]// Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2019: 2723-2732. |
[23] | WANG Z C , LYU Q S , LAN X H ,et al. Cross-lingual knowledge graph alignment via graph convolutional networks[C]// Proceedings of 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2018: 349-357. |
[24] | WU Y T , LIU X , FENG Y S ,et al. Jointly learning entity and relation representations for entity alignment[C]// Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2019: 240-249. |
[25] | XU K , WANG L W , YU M ,et al. Cross-lingual knowledge graph alignment via graph matching neural network[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:Association for Computational Linguistics, 2019: 3156-3161. |
[26] | KOUTRA D , TONG H H , LUBENSKY D . BIG-ALIGN:fast bipartite graph alignment[C]// Proceedings of 2013 IEEE 13th International Conference on Data Mining. Piscataway:IEEE Press, 2013: 389-398. |
[27] | BAYATI M , GLEICH D F , SABERI A ,et al. Message-passing algorithms for sparse network alignment[J]. ACM Transactions on Knowledge Discovery from Data, 2013,7(1): 1-31. |
[28] | CHEN X Y , HEIMANN M , VAHEDIAN F ,et al. CONE-Align:consistent network alignment with proximity-preserving node embedding[C]// Proceedings of the 29th ACM International Conference on Information & Knowledge Management.[S.l.:s.n.], 2020: 1985-1988. |
[29] | ZHU D J , SUN Y D , DU H W ,et al. HUNA:a method of hierarchical unsupervised network alignment for IoT[J]. IEEE Internet of Things Journal, 2021,8(5): 3201-3210. |
[30] | TRUNG H T , VAN VINH T , TAM N T ,et al. Adaptive network alignment with unsupervised and multi-order convolutional networks[C]// Proceedings of 2020 IEEE 36th International Conference on Data Engineering. Piscataway:IEEE Press, 2020: 85-96. |
[31] | SCHOENMACKERS S , ETZIONI O , WELD D S ,et al. Learning first-order Horn clauses from web text[C]// Proceedings of 2010 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2010: 1088-1098. |
[32] | LAO N , COHEN W W . Relational retrieval using a combination of path-constrained random walks[J]. Machine Learning, 2010,81(1): 53-67. |
[33] | KUROKAWA M , . Explainable knowledge reasoning framework using multiple knowledge graph embedding[C]// Proceedings of the 10th International Joint Conference on Knowledge Graphs. New York:ACM Press, 2021: 172-176. |
[34] | ZUO Y K , FANG Q , QIAN S S ,et al. Representation learning of knowledge graphs with entity attributes and multimedia descriptions[C]// Proceedings of 2018 IEEE 4th International Conference on Multimedia Big Data. Piscataway:IEEE Press, 2018: 1-5. |
[35] | XIONG W H , HOANG T , WANG W Y . DeepPath:a reinforcement learning method for knowledge graph reasoning[C]// Proceedings of 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2017: 564-573. |
[36] | 王飞跃 . 平行系统方法与复杂系统的管理和控制[J]. 控制与决策, 2004,19(5): 485-489,514. |
WANG F Y . Parallel system methods for management and control of complex systems[J]. Control and Decision, 2004,19(5): 485-489,514. | |
[37] | 杨林瑶, 陈思远, 王晓 ,等. 数字孪生与平行系统:发展现状、对比及展望[J]. 自动化学报, 2019,45(11): 2001-2031. |
YANG L Y , CHEN S Y , WANG X ,et al. Digital twins and parallel systems:state of the art,comparisons and prospect[J]. Acta Automatica Sinica, 2019,45(11): 2001-2031. | |
[38] | ALMALAQ A , HAO J , ZHANG J J ,et al. Parallel building:a complex system approach for smart building energy management[J]. IEEE/CAA Journal of Automatica Sinica, 2019,6(6): 1452-1461. |
[39] | LI L , LIN Y L , ZHENG N N ,et al. Parallel learning:a perspective and a framework[J]. IEEE/CAA Journal of Automatica Sinica, 2017,4(3): 389-395. |
[40] | 王飞跃, 邱晓刚, 曾大军 ,等. 基于平行系统的非常规突发事件计算实验平台研究[J]. 复杂系统与复杂性科学, 2010,7(4): 1-10. |
WANG F Y , QIU X G , ZENG D J ,et al. A computational experimental platform for emergency response based on parallel systems[J]. Complex Systems and Complexity Science, 2010,7(4): 1-10. | |
[41] | 杨林瑶 . 基于平行学习的多源异构知识协同方法与应用研究[D]. 北京:中国科学院大学, 2022. |
YANG L Y . Methods and applications of multi-source and heterogeneous knowledge collaboration based on parallel learning[D]. Beijing:University of Chinese Academy of Sciences, 2022. | |
[42] | DAI Y X , CHEN Y L , LI X ,et al. Automatic generation of power grid dispatching and control scheme based on heterogeneous information network[C]// Proceedings of 2020 IEEE 4th Conference on Energy Internet and Energy System Integration. Piscataway:IEEE Press, 2020: 3028-3032. |
[43] | SAGI O . Approximating XGBoost with an interpretable decision tree[J]. Information Sciences, 2021,572: 522-542. |
[44] | YANG L Y , WANG X , ZHANG J ,et al. HackGAN:harmonious cross-network mapping using CycleGAN with Wasserstein-Procrustes learning for unsupervised network alignment[J]. IEEE Transactions on Computational Social Systems, 2022,4350(99): 1-14. |
[45] | ZENG W X , ZHAO X , TANG J Y ,et al. Collective entity alignment via adaptive features[C]// Proceedings of 2020 IEEE 36th International Conference on Data Engineering. Piscataway:IEEE Press, 2020: 1870-1873. |
[46] | ZHANG S , TONG H H . FINAL:fast attributed network alignment[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2016: 1345-1354. |
[47] | YANG L Y , LYU C , WANG X ,et al. Collective entity alignment for knowledge fusion of power grid dispatching knowledge graphs[J]. IEEE/CAA Journal of Automatica Sinica, 2022,9(11): 1990-2004. |
[48] | WAN G J , PAN S R , GONG C ,et al. Reasoning like human:hierarchical reinforcement learning for knowledge graph reasoning[C]// Proceedings of the 29th International Joint Conference on Artificial Intelligence. Yokohama:Morgan Kaufmann, 2020: 1926-1932. |
[49] | NEELAKANTAN A , ROTH B , MCCALLUM A . Compositional vector space models for knowledge base completion[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2015: 156-166. |
[50] | YANG L Y , WANG X , DAI Y X ,et al. HackRL:reinforcement learning with hierarchical attention for cross-graph knowledge fusion and collaborative reasoning[J]. Knowledge-Based Systems, 2021,233. |
[51] | PAN S J , YANG Q . A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010,22(10): 1345-1359. |
[52] | SHEN X , DAI Q Y , MAO S T ,et al. Network together:node classification via cross-network deep network embedding[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021,32(5): 1935-1948. |
[53] | YE J H , CHENG H , ZHU Z ,et al. Predicting positive and negative links in signed social networks by transfer learning[C]// Proceedings of the 22nd International Conference on World Wide Web.[S.l.:s.n.], 2013: 1477-1488. |
[54] | YE P J , WANG F Y . Parallel population and parallel human—a cyber-physical social approach[J]. IEEE Intelligent Systems, 2022,37(5): 19-27. |
[55] | WANG F Y , DING W , WANG X ,et al. The DAO to DeSci:AI for free,fair,and responsibility sensitive sciences[J]. IEEE Intelligent Systems, 2022,37(2): 16-22. |
[56] | LI X , YE P , LI J ,et al. From features engineering to scenarios engineering for trustworthy AI:I&I,C&C,and V&V[J]. IEEE Intelligent Systems, 2022,37(4): 18-26. |
[57] | WANG X , YANG J , HAN J ,et al. Metaverses and DeMetaverses:from digital twins in CPS to parallel intelligence in CPSS[J]. IEEE Intelligent Systems, 2022,37(4): 97-102. |
[58] | WANG F Y . Parallel intelligence in metaverses:welcome to Hanoi![J]. IEEE Intelligent Systems, 2022,37(1): 16-20. |
[59] | MITGUTSCH K , ALVARADO N . Purposeful by design? A serious game design assessment framework[C]// Proceedings of the International Conference on the Foundations of Digital Games.[S.l.:s.n.], 2012: 121-128. |
[60] | 李小双, 王晓, 杨林瑶 ,等. 元电网 MetaGrid:基于平行电网的新一代智能电网的体系与架构[J]. 智能科学与技术学报, 2021,3(4): 387-398. |
LI X S , WANG X , YANG L Y ,et al. MetaGrid:a parallel grids based approach for next generation smart power systems[J]. Chinese Journal of Intelligent Science and Technology, 2021,3(4): 387-398. | |
[61] | HUANG Q H , HUANG R K , HAO W T ,et al. Adaptive power system emergency control using deep reinforcement learning[J]. IEEE Transactions on Smart Grid, 2020,11(2): 1171-1182. |
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