平行推理：一种基于ACP方法的虚实互动的知识协同框架

王晓; 杨林瑶; 胡斌; 侯家琛

doi:10.11959/j.issn.2096-6652.202254

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平行推理：一种基于ACP方法的虚实互动的知识协同框架

学术论文 | 更新时间：2024-06-05

- 平行推理：一种基于ACP方法的虚实互动的知识协同框架
- Parallel reasoning: a virtual-real interactive knowledge collaboration framework based on ACP approach
- 智能科学与技术学报 2023年5卷第1期页码：69-82
- 作者机构：
  
  1. 安徽大学人工智能学院，安徽合肥 230601
  2. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
  3. 青岛智能产业技术研究院，山东青岛 256200
  4. 之江实验室图计算研究中心，浙江杭州 311100
  5. 澳门科技大学创新工程学院，澳门 999078
- 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" ]
  [ "杨林瑶（1995– ），男，博士，之江实验室图计算研究中心高级研究专员，主要研究方向为图表示学习、知识融合" ]
  [ "胡斌（1976– ），男, 博士, 青岛智能产业技术研究院执行院长, 主要研究方向为人工智能、自动驾驶和复合协作机器人" ]
  [ "侯家琛（1986– ），男，澳门科技大学博士生，主要研究方向为区块链、Web3.0" ]
- 基金信息：
  
  国家重点研发计划(2018AAA0101502);国家电网有限公司总部科技项目：人在回路的大电网调控混合增强智能基础理论;The Science and Technology Project of State Grid Corporation of China: Fundamental Theory of Human-in-the-loop Hybrid- augmented Intelligence for Power Grid Dispatch and Control
- DOI：10.11959/j.issn.2096-6652.202254
  中图分类号： TP301
- 网络出版日期：2023-03，
  
  纸质出版日期：2023-03-15
- 稿件说明：
移动端阅览
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.
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. DOI： 10.11959/j.issn.2096-6652.202254.

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. DOI： 10.11959/j.issn.2096-6652.202254.

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。

Abstract

Knowledge graph represents empirical knowledge based on structured triples

which can effectively describe the semantic relationships between real-world entities.Knowledge graph has become a critical standard technology of the new generation of artificial intelligence.The development

typical applications of multi-source knowledge graphs

and problems of knowledge collaboration were summarized.A multi-source knowledge graph collaboration framework based on ACP approach

i.e.

parallel reasoning

was proposed

which realized the extraction

fusion

completion

and unbiased application of multi-source heterogeneous knowledge based on artificial systems

computational experiments

and parallel execution.In the end

simulation experiments were conducted on power grid dispatching to evaluate the effectiveness of parallel learning for solving the management and control problems of complex systems.

关键词

Keywords

references

BERNERS-LEE T , HENDLER J , LASSILA O . The semantic web [J ] . Scientific American , 2001 , 284 ( 5 ): 34 - 43 .

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 .

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 .

CHEN X J . A review:knowledge reasoning over knowledge graph [J ] . Expert Systems With Applications , 2020 ,141.

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

RICHENS R H . Report on research:Cambridge language research unit [J ] . Mechanical Translation , 1956 , 3 : 36 - 37 .

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

BERNERS-LEE T , HENDLER J . Publishing on the semantic web [J ] . Nature , 2001 , 410 ( 6832 ): 1023 - 1024 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

LAO N , COHEN W W . Relational retrieval using a combination of path-constrained random walks [J ] . Machine Learning , 2010 , 81 ( 1 ): 53 - 67 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

SAGI O . Approximating XGBoost with an interpretable decision tree [J ] . Information Sciences , 2021 , 572 : 522 - 542 .

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 .

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 .

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 .

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 .

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 .

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 .

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.

PAN S J , YANG Q . A survey on transfer learning [J ] . IEEE Transactions on Knowledge and Data Engineering , 2010 , 22 ( 10 ): 1345 - 1359 .

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 .

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 .

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 .

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 .

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 .

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 .

WANG F Y . Parallel intelligence in metaverses:welcome to Hanoi! [J ] . IEEE Intelligent Systems , 2022 , 37 ( 1 ): 16 - 20 .

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 .

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 .

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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