图神经网络应用于知识图谱推理的研究综述

doi:10.3778/j.issn.1673-9418.2207060

摘要/Abstract

摘要： 知识推理（KR）作为知识图谱构建的一个重要环节，一直是该领域研究的焦点问题。随着知识图谱应用研究的不断深入和范围的不断扩大，将图神经网络（GNN）应用于知识推理的方法能够在获取知识图谱中实体、关系等语义信息的同时，充分考虑知识图谱的结构信息，使其具备更好的可解释性和更强的推理能力，因此近年来受到广泛关注。首先梳理了知识图谱和知识推理的基本知识及研究现状，简要介绍了基于逻辑规则、基于表示学习、基于神经网络和基于图神经网络的知识推理的优势与不足；其次全面总结了基于图神经网络的知识推理最新进展，将基于图神经网络的知识推理按照基于递归图神经网络（RecGNN）、卷积图神经网络（ConvGNN）、图自编码网络（GAE）和时空图神经网络（STGNN）的知识推理进行分类，对各类典型网络模型进行了介绍和对比分析；然后介绍了基于图神经网络的知识推理在医学、智能制造、军事、交通等领域的应用；最后提出了基于图神经网络的知识推理的未来研究方向，并对这个快速增长领域中的各方向研究进行了展望。

关键词: 知识图谱, 知识推理（KR）, 图神经网络（GNN）, 语义信息, 结构信息

Abstract: As an important element of knowledge graph construction, knowledge reasoning (KR) has always been a hot topic of research. With the deepening of knowledge graph application research and the expanding of its scope, graph neural network (GNN) based KR methods have received extensive attention due to their capability of obtaining semantic information such as entities and relationships in knowledge graph, high interpretability, and strong reasoning ability. In this paper, firstly, basic knowledge and research status of knowledge graph and KR are summarized. The advantages and disadvantages of KR approaches based on logic rules, representation learning, neural network and graph neural network are briefly introduced. Secondly, the latest progress in KR based on GNN is comprehensively summarized. GNN-based KR methods are categorized into knowledge reasoning based on recurrent graph neural networks (RecGNN), convolutional graph neural networks (ConvGNN), graph auto-encoders (GAE) and spatial-temporal graph neural networks (STGNN). Various typical network models are introduced and compared. Thirdly, this paper introduces the application of KR based on graph neural network in health care, intelligent manufacturing, military, transportation, etc. Finally, the future research directions of GNN-based KR are proposed, and related research in various directions in this rapidly growing field is discussed.

Key words: knowledge graph, knowledge reasoning (KR), graph neural network (GNN), semantic information;structural information

孙水发, 李小龙, 李伟生, 雷大江, 李思慧, 杨柳, 吴义熔. 图神经网络应用于知识图谱推理的研究综述[J]. 计算机科学与探索, 2023, 17(1): 27-52.

SUN Shuifa, LI Xiaolong, LI Weisheng, LEI Dajiang, LI Sihui, YANG Liu, WU Yirong. Review of Graph Neural Networks Applied to Knowledge Graph Reasoning[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(1): 27-52.

参考文献

[1] RICHENS R H. Preprogramming for mechanical translation[J]. Mechanical Translation, 1956, 3(1): 20-25.
[2] JI S, PAN S, CAMBRIA E, et al. A survey on knowledge graphs: representation, acquisition, and applications[J]. IEEE Tran-sactions on Neural Networks and Learning Systems, 2021, 33(2): 494-514.
[3] 马昂, 于艳华, 杨胜利, 等. 基于强化学习的知识图谱研究综述[J]. 计算机研究与发展, 2022, 59(8): 1694-1722.
MA A, YU Y H, YANG S L, et al. Survey of knowledge graph based on reinforcement learning[J]. Journal of Com-puter Research and Development, 2022, 59(8): 1694-1722.
[4] NEWELL A, SHAW J C, SIMON H A. Report on a general problem solving program[C]//Proceedings of the 1st Inter-national Conference on Information Processing, Jun 15-20,1959. Paris: UNESCO, 1959: 256-264.
[5] CHEN Z, WANG Y, ZHAO B, et al. Knowledge graph com-pletion: a review[J]. IEEE Access, 2020, 8: 192435-192456.
[6] ARORA S. A survey on graph neural networks for know-ledge graph completion[J]. arXiv:2007.12374, 2020.
[7] ZHANG Z, ZHUANG F Z, ZHU H S, et al. Relational graph neural network with hierarchical attention for knowledge graph completion[C]//Proceedings of the 34th AAAI Confe-rence on Artificial Intelligence, the 32nd Innovative Appli-cations of Artificial Intelligence Conference, the 10th AAAI Symposium on Educational Advances in Artificial Intelli-gence, New York, Feb 7-12, 2020. Menlo Park: AAAI, 2020: 9612-9619.
[8] CAI B, XIANG Y, GAO L, et al. Temporal knowledge graph completion: a survey[J]. arXiv:2201.08236, 2022.
[9] ZEB A, SAIF S, CHEN J, et al. Complex graph convolu-tional network for link prediction in knowledge graphs[J]. Expert Systems with Applications, 2022, 200: 116796.
[10] 陈子睿, 王鑫, 王林, 等. 开放领域知识图谱问答研究综述[J]. 计算机科学与探索, 2021, 15(10): 1843-1869.
CHEN Z R, WANG X, WANG L, et al. Survey of open-domain knowledge graph question answering[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(10): 1843-1869.
[11] 萨日娜, 李艳玲, 林民. 知识图谱推理问答研究综述[J]. 计算机科学与探索, 2022, 16(8): 1727-1741.
SA R N, LI Y L, LIN M. Survey of question answering based on knowledge graph reasoning[J]. Journal of Fron-tiers of Computer Science and Technology, 2022, 16(8): 1727-1741.
[12] GUO Q, ZHUANG F, QIN C, et al. A survey on knowledge graph-based recommender systems[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(8): 3549-3568.
[13] 朱冬亮, 文奕, 万子琛. 基于知识图谱的推荐系统研究综述[J]. 数据分析与知识发现, 2021, 5(12): 1-13.
ZHU D L, WEN Y, WAN Z C. Review of recommendation systems based on knowledge graph[J]. Data Analysis and Knowledge Discovery, 2021, 5(12):1-13.
[14] LIU J, DUAN L. A survey on knowledge graph-based re-commender systems[C]//Proceedings of the 2021 IEEE 5th Advanced Information Technology, Electronic and Auto-mation Control Conference, Xi??an, Oct 15-17, 2021. Pis-cataway: IEEE, 2021: 2450-2453.
[15] 程章桃, 钟婷, 张晟铭, 等. 基于图学习的推荐系统研究综述[J]. 计算机科学, 2022, 49(9): 1-13.
CHENG Z T, ZHONG T, ZHANG S M, et al. Survey of re-commendation systems based on graph learning[J]. Compu-ter Science, 2022, 49(9): 1-13.
[16] 田萱, 陈杭雪. 推荐任务中知识图谱嵌入应用研究综述[J]. 计算机科学与探索, 2022, 16(8): 1681-1705.
TIAN X, CHEN H X. Survey on applications of knowledge graph embedding in recommendation tasks[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1681-1705.
[17] 乔凯, 陈可佳, 陈景强. 基于知识图谱与关键词注意机制的中文医疗问答匹配方法[J]. 模式识别与人工智能, 2021, 34(8): 733-741.
QIAO K, CHEN K J, CHEN J Q. Chinese medical question answering matching method based on knowledge graph and keyword attention mechanism[J]. Pattern Recognition and Artificial Intelligence, 2021, 34(8): 733-741.
[18] 范媛媛, 李忠民. 中文医学知识图谱研究及应用进展[J]. 计算机科学与探索, 2022, 16(10): 2219-2233.
FAN Y Y, LI Z M. Research and application progress of Chinese medical knowledge graph[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(10): 2219-2233.
[19] 董文波, 孙仕亮, 殷敏智. 医学知识推理研究现状与发展[J]. 计算机科学与探索, 2022, 16(6): 1193-1213.
DONG W B, SUN S L, YIN M Z. Research and develop-ment of medical knowledge graph reasoning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(6): 1193-1213.
[20] 袁俊, 刘国柱, 梁宏涛, 等. 知识图谱在商业银行风控领域的研究与应用综述[J]. 计算机工程与应用, 2022, 58(19): 37-52.
YUAN J, LIU G Z, LIANG H T, et al. Summary of resea-rch and application of knowledge graphs in risk manage-ment field of commercial banks[J]. Computer Engineering and Applications, 2022, 58(19): 37-52.
[21] 张栋豪, 刘振宇, 郏维强, 等. 知识图谱在智能制造领域的研究现状及其应用前景综述[J]. 机械工程学报, 2021, 57(5): 90-113.
ZHANG D H, LIU Z Y, JIA W Q, et al. A review on know-ledge graph and its application prospects to intelligent manu-facturing[J]. Journal of Mechanical Engineering, 2021, 57(5): 90-113.
[22] 丁兆云, 刘凯, 刘斌, 等. 网络安全知识图谱研究综述[J]. 华中科技大学学报(自然科学版), 2021, 49(7): 79-91.
DING Z Y, LIU K, LIU B, et al. Survey of cyber security knowledge graph[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2021, 49(7): 79-91.
[23] CHEN X, JIA S, XIANG Y. A review: knowledge reasoning over knowledge graph[J]. Expert Systems with Applica-tions, 2020, 141: 112948.
[24] TIAN L, ZHOU X, WU Y P, et al. Knowledge graph and knowledge reasoning: a systematic review[J]. Journal of Electronic Science and Technology, 2022: 100159.
[25] YE Z, KUMAR Y J, SING G O, et al. A comprehensive survey of graph neural networks for knowledge graphs[J]. IEEE Access, 2022, 10: 75729-75741.
[26] 宋浩楠, 赵刚, 孙若莹. 基于深度强化学习的知识推理研究进展综述[J]. 计算机工程与应用, 2022, 58(1): 12-25.
SONG H N, ZHAO G, SUN R Y. Developments of know-ledge reasoning based on deep reinforcement learning[J]. Computer Engineering and Applications, 2022, 58(1): 12-25.
[27] 张宇, 郭文忠, 林森, 等. 深度学习与知识推理相结合的研究综述[J]. 计算机工程与应用, 2022, 58(1): 56-69.
ZHANG Y, GUO W Z, LIN S, et al. Review on combina-tion of deep learning and knowledge reasoning[J]. Compu-ter Engineering and Applications, 2022, 58(1): 56-69.
[28] ZHANG W, CHEN J, LI J, et al. Knowledge graph reaso-ning with logics and embeddings: survey and perspective[J]. arXiv:2202.07412, 2022.
[29] 马瑞新, 李泽阳, 陈志奎, 等. 知识图谱推理研究综述[J]. 计算机科学, 2022, 49(S1): 74-85.
MA R X, LI Z Y, CHEN Z K, et al. Review of reasoning on knowledge graph[J]. Computer Science, 2022, 49(S1): 74-85.
[30] CAI H, ZHENG V W, CHANG K C C. A comprehensive survey of graph embedding: problems, techniques, and app-lications[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(9): 1616-1637.
[31] CAO S S, LU W, XU Q K. GraRep: learning graph repre-sentations with global structural information[C]//Procee-dings of the 24th ACM International Conference on Infor-mation and Knowledge Management, Melbourne, Oct 19-23, 2015. New York: ACM, 2015: 891-900.
[32] 赵军. 知识图谱[M]. 北京: 高等教育出版社, 2018.
ZHAO J. Knowledge graph[M]. Beijing: Higher Education Press, 2018.
[33] ZHU C C, CHEN M H, FAN C J, et al. Learning from history: modeling temporal knowledge graphs with sequen-tial copy-generation networks[C]//Proceedings of the 35th AAAI Conference on Artificial Intelligence, the 33rd Con-ference on Innovative Applications of Artificial Intelli-gence, the 11th Symposium on Educational Advances in Artificial Intelligence. Menlo Park: AAAI, 2021: 4732-4740.
[34] 张仲伟, 曹雷, 陈希亮, 等. 基于神经网络的知识推理研究综述[J]. 计算机工程与应用, 2019, 55(12): 8-19.
ZHANG Z W, CAO L, CHEN X L, et al. Survey of know-ledge reasoning based on neural network[J]. Computer Engineering and Applications, 2019, 55(12): 8-19.
[35] 官赛萍, 靳小龙, 贾岩涛, 等. 面向知识图谱的知识推理研究进展[J]. 软件学报, 2018, 29(10): 2966-2994.
GUAN S P, JIN X L, JIA Y T, et al. Knowledge reasoning over knowledge graph: a survey[J]. Journal of Software, 2018, 29(10): 2966-2994.
[36] 翁金塔, 仇晶, 张光华. 面向推理的知识图谱表示学习方法综述[J]. 广州大学学报(自然科学版), 2021, 20(3): 80-89.
WENG J T, QIU J, ZHANG G H. The representation lear-ning method of a knowledge graph for reasoning: a review[J]. Journal of Guangzhou University (Natural Science Edi-tion), 2021, 20(3): 80-89.
[37] WU Z, PAN S, CHEN F, et al. A comprehensive survey on graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(1): 4-24.
[38] LEE T W, LEWICKI M S, GIROLAMI M, et al. Blind sou-rce separation of more sources than mixtures using over-complete representations[J]. IEEE Signal Processing Let-ters, 1999, 6(4): 87-90.
[39] SCHOENMACKERS S, DAVIS J, ETZIONI O, et al. Lear-ning first-order horn clauses from web text[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, Oct 9-11, 2010. Strouds-burg: ACL, 2010: 1088-1098.
[40] GALáRRAGA L A, TEFLIOUDI C, HOSE K, et al. AMIE: association rule mining under incomplete evidence in onto-logical knowledge bases[C]//Proceedings of the 22nd Inter-national Conference on World Wide Web, Rio de Janeiro, May 13-17, 2013. New York: ACM, 2013: 413-422.
[41] MITCHELL T, COHEN W, HRUSCHKA E, et al. Never-ending learning[J]. Communications of the ACM, 2018, 61(5): 103-115.
[42] WANG W Y, MAZAITIS K, COHEN W W. Programming with personalized pagerank: a locally groundable first-order probabilistic logic[C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, San Francisco, Oct 27-Nov 1, 2013. New York: ACM, 2013: 2129-2138.
[43] RICHARDSON M, DOMINGOS P. Markov logic networks[J]. Machine Learning, 2006, 62(1): 107-136.
[44] CHEN Y, WANG D Z. Knowledge expansion over probabi-listic knowledge bases[C]//Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, Snowbird Utah, Jun 22-27, 2014. New York: ACM, 2014: 649-660.
[45] KIMMIG A, BACH S, BROECHELER M, et al. A short introduction to probabilistic soft logic[C]//Proceedings of the 2012 NIPS Workshop on Probabilistic Programming: Foundations and Applications, Lake Tahoe, Dec 8, 2012.Cambridge: MIT Press, 2012: 1-4.
[46] BACH S H, BROECHELER M, HUANG B, et al. Hinge-loss Markov random fields and probabilistic soft logic[J]. Journal of Machine Learning Research, 2017, 18: 1-67.
[47] PUJARA J, MIAO H, GETOOR L, et al. Ontology-aware partitioning for knowledge graph identification[C]//Procee-dings of the 2013 Workshop on Automated Knowledge Base Construction, San Francisco, Oct 27-28, 2013. New York: ACM, 2013: 19-24.
[48] CHEN Y, GOLDBERG S, WANG D Z, et al. Ontological pathfinding[C]//Proceedings of the 2016 International Con-ference on Management of Data, San Francisco, Jun 26-Jul 1, 2016. New York: ACM, 2016: 835-846.
[49] WEI Y Z, LUO J, XIE H Y. KGRL: an OWL2 RL reaso-ning system for large scale knowledge graph[C]//Procee-dings of the 12th International Conference on Semantics, Knowledge and Grids, Beijing, Aug 15-17, 2016. Washing-ton: IEEE Computer Society, 2016: 83-89.
[50] LAO N, COHEN W W. Relational retrieval using a com-bination of path-constrained random walks[J]. Machine Lear-ning, 2010, 81(1): 53-67.
[51] GARDNER M, MITCHELL T. Efficient and expressive know-ledge base completion using subgraph feature extraction[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Sep 17-21, 2015. Stroudsburg: ACL, 2015: 1488-1498.
[52] WANG Q, LIU J, LUO Y F, et al. Knowledge base com-pletion via coupled path ranking[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Aug 7-12, 2016. Stroudsburg: ACL, 2016: 1308-1318.
[53] NICKEL M, TRESP V, KRIEGEL H P. A three-way model for collective learning on multi-relational data[C]//Procee-dings of the 28th International Conference on Machine Learning, Bellevue, Jun 28-Jul 2, 2011. Madison: Omni-press, 2011: 809-816.
[54] NICKEL M, TRESP V, KRIEGEL H P. Factorizing YAGO: scalable machine learning for linked data[C]//Proceedings of the 21st International Conference on World Wide Web, Lyon, Apr 16-20, 2012. New York: ACM, 2012: 271-280.
[55] WU Y, ZHU D, LIAO X, et al. Knowledge graph reasoning based on paths of tensor factorization[J]. Pattern Recogni-tion and Artificial Intelligence, 2017, 30(5): 473-480.
[56] JAIN P, MURTY S, CHAKRABARTI S. Joint matrix-tensor factorization for knowledge base inference[J]. arXiv:1706. 00637, 2017.
[57] BORDES A, USUNIER N, GARCíA-DURáN A, et al. Translating embeddings for modeling multi-relational data[C]//Proceedings of the 27th Annual Conference on Neural Information Processing Systems 2013, Dec 5-8, 2013. Red Hook: Curran Associates, 2013: 2787-2795.
[58] WANG Z, ZHANG J W, FENG J L, et al. Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the 14th AAAI Conference on Artificial Intelligence Québec, Jul 27-31, 2014. Menlo Park: AAAI, 2014: 1112-1119.
[59] LIN Y, LIU Z, SUN M, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Procee-dings of the 15th AAAI Conference on Artificial Intel-ligence, Austin, Jan 25-30, 2015. Menlo Park: AAAI, 2015: 2181-2187.
[60] LIN Y, LIU Z, LUAN H, et al. Modeling relation paths for representation learning of knowledge bases[J]. arXiv:1506. 00379, 2015.
[61] JI G, HE S, XU L, et al. Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, Jul 26-31, 2015. Stroud-sburg: ACL, 2015: 687-696.
[62] JI G L, LIU K, HE S Z, et al. Knowledge graph completion with adaptive sparse transfer matrix[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, Feb 12-17, 2016. Menlo Park: AAAI, 2016: 985-991.
[63] XIAO H, HUANG M, HAO Y, et al. TransG: a generative mixture model for knowledge graph embedding[C]//Procee-dings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Aug 7-12, 2016. Strouds-burg: ACL, 2016: 2316-2325.
[64] TRIVEDI R, DAI H J, WANG Y C, et al. Know-evolve: deep temporal reasoning for dynamic knowledge graphs[C]//Proceedings of the 34th International Conference on Machine Learning, Sydney, Aug 6-11, 2017: 3462-3471.
[65] DASGUPTA S S, RAY S N, TALUKDAR P. HyTE: hyper-plane-based temporally aware knowledge graph embedding[C]//Proceedings of the 2018 Conference on Empirical Me-thods in Natural Language Processing, Brussels, Oct 31-Nov 4, 2018. Stroudsburg: ACL, 2018: 2001-2011.
[66] CHEN X, CHEN M, SHI W, et al. Embedding uncertain knowledge graphs[C]//Proceedings of the 33rd AAAI Con-ference on Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 3363-3370.
[67] BORDES A, GLOROT X, WESTON J, et al. A semantic matching energy function for learning with multi-relational data[J]. Machine Learning, 2014, 94(2): 233-259.
[68] YANG B, YIH W, HE X, et al. Embedding entities and relations for learning and inference in knowledge bases[J]. arXiv:1412.6575, 2014.
[69] NICKEL M, ROSASCO L, POGGIO T. Holographic em-beddings of knowledge graphs[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, Feb 12-17, 2016. Menlo Park: AAAI, 2016: 1955-1961.
[70] TROUILLON T, DANCE C R, WELBL J, et al. Knowle-dge graph completion via complex tensor factorization[J]. Journal of Machine Learning Research, 2017, 8: 1-38.
[71] GUO S, WANG Q, WANG L, et al. Jointly embedding knowledge graphs and logical rules[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Lan-guage Processing, Austin, Nov 1-4, 2016. Stroudsburg: ACL, 2016: 192-202.
[72] WANG Z, LI J, LIU Z, et al. Text-enhanced representation learning for knowledge graph[C]//Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York, Jul 9-15, 2016. Palo Alto: AAAI, 2016: 4-17.
[73] QU M, TANG J. Probabilistic logic neural networks for reasoning[C]//Advances in Neural Information Processing Systems 32, Vancouver, Dec 8-14, 2019: 7710-7720.
[74] ZHANG W, PAUDEL B, WANG L, et al. Iteratively lear-ning embeddings and rules for knowledge graph reasoning[C]//Proceedings of the World Wide Web Conference, San Francisco, May 13-17, 2019. New York: ACM, 2019: 2366-2377.
[75] XIE R, LIU Z, JIA J, et al. Representation learning of know-ledge graphs with entity descriptions[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoe-nix, Feb 12-17, 2016. Menlo Park: AAAI, 2016: 2659-2665.
[76] SHI B, WENINGER T. Open-world knowledge graph com-pletion[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, Feb 2-7, 2018. Menlo Park: AAAI, 2018: 1957-1964.
[77] TAY Y, TUAN L A, PHAN M C, et al. Multi-task neural network for non-discrete attribute prediction in knowledge graphs[C]//Proceedings of the 2017 ACM Conference on Information and Knowledge Management, Singapore, Nov 6-10, 2017. New York: ACM, 2017: 1029-1038.
[78] DETTMERS T, MINERVINI P, STENETORP P, et al. Con-volutional 2D knowledge graph embeddings[C]//Procee-dings of the 32nd AAAI Conference on Artificial Intelli-gence, New Orleans, Feb 2-7, 2018. Menlo Park: AAAI, 2018: 1811-1818.
[79] LI S J, CHEN S D, OUYANG X Y, et al. Joint learning based on multi-shaped filters for knowledge graph completion[J]. High Technology Letters, 2021, 27(1): 43-52.
[80] NEELAKANTAN A, ROTH B, MCCALLUM A. Compo-sitional vector space models for knowledge base inference[C]//Proceedings of the 29th AAAI Conference on Artifi-cial Intelligence, Austin, Jan 25-30, 2015. Menlo Park: AAAI, 2015: 31-34.
[81] SHEN Y, HUANG P S, CHANG M W, et al. Traversing knowledge graph in vector space without symbolic space guidance[J]. arXiv:1611.04642, 2016.
[82] GUO L B, ZHANG Q H, GE W Y, et al. DSKG: a deep se-quential model for knowledge graph completion[C]//Pro-ceedings of the 3rd China Conference on Knowledge Graph and Semantic Computing, Tianjin, Aug 14-17, 2018. Cham: Springer, 2018: 65-77.
[83] XIONG W, HOANG T, WANG W Y. DeepPath: a reinfor-cement learning method for knowledge graph reasoning[J]. arXiv:1707.06690, 2017.
[84] DAS R, DHULIAWALA S, ZAHEER M, et al. Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning[J]. arXiv:1711.05851, 2017.
[85] LI Z, JIN X, GUAN S, et al. Path reasoning over know-ledge graph: a multi-agent and reinforcement learning based method[C]//Proceedings of the 2018 IEEE International Conference on Data Mining, Singapore, Nov 17-20, 2018. Piscataway: IEEE, 2018: 929-936.
[86] WANG Q, JI Y, HAO Y, et al. GRL: knowledge graph com-pletion with GAN-based reinforcement learning[J]. Know-ledge-Based Systems, 2020, 209: 106421.
[87] TIWARI P, ZHU H, PANDEY H M. DAPath: distance-aware knowledge graph reasoning based on deep reinfor-cement learning[J]. Neural Networks, 2021, 135: 1-12.
[88] SCARSELLI F, GORI M, TSOI A C, et al. The graph neural network model[J]. IEEE Transactions on Neural Networks, 2008, 20(1): 61-80.
[89] GALLICCHIO C, MICHELI A. Graph echo state networks[C]//Proceedings of the 2010 International Joint Confe-rence on Neural Networks, Barcelona, Jul 18-23, 2010. Pis-cataway: IEEE, 2010: 1-8.
[90] LI Y, TARLOW D, BROCKSCHMIDT M, et al. Gated graph sequence neural networks[J]. arXiv:1511.05493, 2015.
[91] DAI H J, KOZAREVA Z, DAI B, et al. Learning steady-states of iterative algorithms over graphs[C]//Proceedings of the 35th International Conference on Machine Learning, Stock-holmsm?ssan, Jul 10-15, 2018: 1114-1122.
[92] BRUNA J, ZAREMBA W, SZLAM A, et al. Spectral net-works and locally connected networks on graphs[J]. arXiv:1312.6203, 2013.
[93] HAMILTON W L, YING Z T, LESKOVEC J. Inductive representation learning on large graphs[C]//Advances in Neural Information Processing Systems 30, Long Beach, Dec 4-9, 2017. Red Hook: Curran Associates, 2017: 1024-1034.
[94] SCHLICHTKRULL M S, KIPF T N, BLOEM P, et al. Mo-deling relational data with graph convolutional networks[C]//LNCS 10843: Proceedings of the 15th European Se-mantic Web Conference, Heraklion, Jun 3-7, 2018. Cham: Springer, 2018: 593-607.
[95] VASHISHTH S, SANYAL S, NITIN V, et al. Composi-tion based multi-relational graph convolutional networks[J]. arXiv:1911.03082, 2019.
[96] SHANG C, TANG Y, HUANG J, et al. End-to-end structure-aware convolutional networks for knowledge base com-pletion[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, the 9th AAAI Sympo-sium on Educational Advances in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 3060-3067.
[97] VELI?KOVI? P, CUCURULL G, CASANOVA A, et al. Graph attention networks[J]. arXiv:1710.10903, 2017.
[98] ZHANG J, SHI X, XIE J, et al. GaAN: gated attention networks for learning on large and spatio-temporal graphs[J]. arXiv:1803.07294, 2018.
[99] PARK N, KAN A, DONG X L, et al. Estimating node im-portance in knowledge graphs using graph neural networks[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 596-606.
[100] CHEN X, DING L, XIANG Y. Neighborhood aggregation based graph attention networks for open-world knowledge graph reasoning[J]. Journal of Intelligent & Fuzzy Sys-tems, 2021, 41(2): 3797-3808.
[101] CAO S, LU W, XU Q. Deep neural networks for learning graph representations[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, Feb 12-17, 2016. Menlo Park: AAAI, 2016: 1145-1152.
[102] WANG D, CUI P, ZHU W. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York:ACM, 2016: 1225-1234.
[103] KIPF T N, WELLING M. Variational graph auto-encoders[J]. arXiv:1611.07308, 2016.
[104] PAN S, HU R, LONG G, et al. Adversarially regularized graph autoencoder for graph embedding[J]. arXiv:1802. 04407, 2018.
[105] LI Y, VINYALS O, DYER C, et al. Learning deep genera-tive models of graphs[J]. arXiv:1803.03324, 2018.
[106] BOJCHEVSKI A, SHCHUR O, ZüGNER D, et al. Net-GAN: generating graphs via random walks[C]//Procee-dings of the 35th International Conference on Machine Learning, Stockholm, Jul 10-15, 2018: 610-619.
[107] WANG S, WEI X, NOGUEIRADOS SANTOS C N, et al. Mixed-curvature multi-relational graph neural network for knowledge graph completion[C]//Proceedings of the 30th Web Conference 2021, Ljubljana, Apr 19-23, 2021. New York: ACM, 2021: 1761-1771.
[108] LI Y, YU R, SHAHABI C, et al. Diffusion convolutional recurrent neural network: data-driven traffic forecasting[J]. arXiv:1707.01926, 2017.
[109] YU B, YIN H, ZHU Z. Spatio-temporal graph convolu-tional networks: a deep learning framework for traffic forecasting[J]. arXiv:1709.04875, 2017.
[110] WU Z, PAN S, LONG G, et al. Graph wavenet for deep spatial-temporal graph modeling[J]. arXiv:1906.00121, 2019.
[111] ZHENG C P, FAN X L, WANG C, et al. GMAN: a graph multi-attention network for traffic prediction[C]//Procee-dings of the 34th AAAI Conference on Artificial Intelli-gence, the 32nd Innovative Applications of Artificial Intelligence Conference, the 10th AAAI Symposium on Educational Advances in Artificial Intelligence, New York, Feb 7-12, 2020. Menlo Park: AAAI, 2020: 1234-1241.
[112] KHALED A, ELSIR A M T, SHEN Y. TFGAN: traffic fore-casting using generative adversarial network with multi-graph convolutional network[J]. Knowledge-Based Sys-tems, 2022: 108990.
[113] GORI M, MONFARDINI G, SCARSELLI F. A new model for learning in graph domains[C]//Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, Jul 31-Aug 4, 2005. Piscataway: IEEE, 2005: 729-734.
[114] LI J H, XU W B, JIN Y W, et al. Applying of graph neural network in relationship prediction in knowledge graph reasoning[C]//Proceedings of the 2021 IEEE 23rd Inter-national Conference on High Performance Computing & Communications; the 7th International Conference on Data Science & Systems; the 19th International Con-ference on Smart City; the 7th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Application, Haikou, Dec 20-22, 2021. Piscataway: IEEE, 2021: 2206-2210.
[115] HENAFF M, BRUNA J, LECUN Y. Deep convolutional networks on graph-structured data[J]. arXiv:1506.05163, 2015.
[116] DEFFERRARD M, BRESSON X, VANDERGHEYNST P. Convolutional neural networks on graphs with fast loca-lized spectral filtering[C]//Advances in Neural Informa-tion Processing Systems 29, Barcelona, Dec 5-10, 2016. Red Hook: Curran Associates, 2016: 3837-3845.
[117] LEVIE R, MONTI F, BRESSON X, et al. CayleyNets: graph convolutional neural networks with complex ratio-nal spectral filters[J]. IEEE Transactions on Signal Proces-sing, 2018, 67(1): 97-109.
[118] CHEN J, MA T, XIAO C. FastGCN: fast learning with graph convolutional networks via importance sampling[J]. arXiv:1801.10247, 2018.
[119] HUR A, JANJUA N, AHMED M. A survey on state-of-the-art techniques for knowledge graphs construction and chal-lenges ahead[C]//Proceedings of the IEEE 4th Interna-tional Conference on Artificial Intelligence and Know-ledge Engineering, Laguna Hills, Dec 1-3, 2021. Pisca-taway: IEEE, 2021: 99-103.
[120] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[J]. arXiv:1609.02907, 2016.
[121] DAI H, DAI B, SONG L. Discriminative embeddings of latent variable models for structured data[C]//Proceedings of the 33rd International Conference on Machine Lear-ning, New York, Jun 19-24, 2016: 2702-2711.
[122] HU F, ZHU Y, WU S, et al. Hierarchical graph convolu-tional networks for semi-supervised node classification[J]. arXiv:1902.06667, 2019.
[123] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Proces-sing Systems 30, Long Beach, Dec 4-9, 2017. Red Hook: Curran Associates, 2017: 5998-6008.
[124] XIE Y, ZHANG Y, GONG M, et al. MGAT: multi-view graph attention networks[J]. Neural Networks, 2020, 132: 180-189.
[125] XU D, RUAN C, KORPEOGLU E, et al. Inductive repre-sentation learning on temporal graphs[J]. arXiv:2002. 07962, 2020.
[126] NATHANI D, CHAUHAN J, SHARMA C, et al. Learning attention-based embeddings for relation prediction in know-ledge graphs[J]. arXiv:1906.01195, 2019.
[127] XU X, FENG W, JIANG Y, et al. Dynamically pruned message passing networks for large-scale knowledge graph reasoning[J]. arXiv:1909.11334, 2019.
[128] XIE Z, ZHOU G, LIU J, et al. ReInceptionE: relation-aware inception network with joint local-global structural information for knowledge graph embedding[C]//Procee-dings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2020: 5929-5939.
[129] 康世泽, 吉立新, 张建朋. 一种基于图注意力网络的异质信息网络表示学习框架[J]. 电子与信息学报, 2021, 43(4): 915-922.
KANG S Z, JI L X, ZHANG J P. Heterogeneous informa-tion network representation learning framework based on graph attention network[J]. Journal of Electronics & Infor-mation Technology, 2021, 43(4): 915-922.
[130] 田玲, 张谨川, 张晋豪, 等. 知识图谱综述——表示、构建、推理与知识超图理论[J]. 计算机应用, 2021, 41(8): 2161-2186.
TIAN L, ZHANG J C, ZHANG J H, et al. Knowledge graph survey：representation，construction, reasoning and knowledge hypergraph theory[J]. Journal of Computer App-lications, 2021, 41(8): 2161-2186.
[131] SHANG C, LIU Q, CHEN K S, et al. Edge attention-based multi-relational graph convolutional networks[J]. arXiv: 1802.04944, 2018.
[132] ZHANG Y, CHEN X, YANG Y, et al. Efficient proba-bilistic logic reasoning with graph neural networks[J]. arXiv:2001.11850, 2020.
[133] CHAMI I, WOLF A, JUAN D C, et al. Low-dimensional hyperbolic knowledge graph embeddings[J]. arXiv:2005. 00545, 2020.
[134] YU W, ZHENG C, CHENG W, et al. Learning deep network representations with adversarially regularized autoenco-ders[C]//Proceedings of the 24th ACM SIGKDD Inter-national Conference on Knowledge Discovery and Data Mining, London, Aug 19-23, 2018. New York: ACM, 2018: 2663-2671.
[135] TU K, CUI P, WANG X, et al. Deep recursive network em-bedding with regular equivalence[C]//Proceedings of the 24th ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining, London, Aug 19-23, 2018. New York: ACM, 2018: 2357-2366.
[136] DE CAO N, KIPF T. MolGAN: an implicit generative model for small molecular graphs[J]. arXiv:1805.11973, 2018.
[137] YOU J, YING R, REN X, et al. GraphRNN: generating realistic graphs with deep auto-regressive models[C]//Pro-ceedings of the 35th International Conference on Ma-chine Learning, Stockholm, Jul 10-15, 2018: 5708-5717.
[138] ZHAO L, SONG Y, ZHANG, et al. T-GCN: a temporal graph convolutional network for traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(9): 3848-3858.
[139] SEO Y, DEFFERRARD M, VANDERGHEYNST P, et al. Structured sequence modeling with graph convolutional recurrent networks[C]//LNCS 11301: Proceedings of the 25th International Conference on Neural Information Pro-cessing, Siem Reap, Dec 13-16, 2018. Cham: Springer, 2018: 362-373.
[140] GUO S, LIN Y, FENG N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, the 9th AAAI Sym-posium on Educational Advances in Artificial Intelligence,Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 922-929.
[141] WU Z, PAN S, LONG G, et al. Connecting the dots: multivariate time series forecasting with graph neural networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 753-763.
[142] VRETINARIS A, LEI C, EFTHYMIOU V, et al. Medical entity disambiguation using graph neural networks[C]//Proceedings of the 2021 International Conference on Management of Data. New York: ACM, 2021: 2310-2318.
[143] ZITNIK M, AGRAWAL M, LESKOVEC J. Modeling polypharmacy side effects with graph convolutional net-works[J]. Bioinformatics, 2018, 34(13): i457-i466.
[144] IOANNIDIS V N, MARQUES A G, GIANNAKIS G B. Graph neural networks for predicting protein functions[C]//Proceedings of the 2019 IEEE 8th International Work-shop on Computational Advances in Multi-Sensor Adap-tive Processing, Le Gosier, Dec 15-18, 2019. Piscataway: IEEE, 2019: 221-225.
[145] LI L, WANG P, YAN J, et al. Real-world data medical knowledge graph: construction and applications[J]. Artifi-cial Intelligence in Medicine, 2020, 103: 101817.
[146] ZHANG Y. Knowledge reasoning with graph neural net-works[D]. Atlanta: Georgia Institute of Technology, 2021.
[147] 黄超. 基于图神经网络的知识推理研究与应用[D]. 成都: 电子科技大学, 2021.
HUANG C. Research and application of knowledge rea-soning based on graph neural network[D]. Chengdu: Uni-versity of Electronic Science and Technology of China, 2021.
[148] MA Y, HE Z, LI W, et al. Understanding graphs in EDA: from shallow to deep learning[C]//Proceedings of the 2020 International Symposium on Physical Design, Taipei, China, Sep 20-23, 2020. New York: ACM, 2020: 119-126.
[149] 林旺群, 汪淼, 王伟, 等. 知识图谱研究现状及军事应用[J]. 中文信息学报, 2020, 34(12): 9-16.
LIN W Q, WANG M, WANG W, et al. A survey to know-ledge graph and its military application[J]. Journal of Chinese Information Processing, 2020, 34(12): 9-16.
[150] 张清辉, 杨楠, 梁政. 任务驱动的军事信息服务知识推理研究[J]. 火力与指挥控制, 2021, 46(5): 64-70.
ZHANG Q H, YANG N, LIANG Z. Study on knowledge reasoning of task driven military information service[J]. Fire Control & Command Control, 2021, 46(5): 64-70.
[151] 庞维建, 李辉, 黄谦, 等. 基于本体的无人系统任务规划研究综述[J]. 系统工程与电子技术, 2022, 44(3): 908-920.
PANG W J, LI H, HUANG Q, et al. Review on ontology-based task planning for unmanned systems[J]. System Engineering and Electronics, 2022, 44(3): 908-920.
[152] PENG H, WANG H, DU B, et al. Spatial temporal inci-dence dynamic graph neural networks for traffic flow forecasting[J]. Information Sciences, 2020, 521: 277-290.
[153] SANKAR A, WU Y, GOU L, et al. DySAT: deep neural rep-resentation learning on dynamic graphs via self-attention networks[C]//Proceedings of the 13th International Confe-rence on Web Search and Data Mining, Houston, Feb 3-7, 2020. New York: ACM, 2020: 519-527.
[154] PAREJA A, DOMENICONI G, CHEN J, et al. Evolve-GCN: evolving graph convolutional networks for dynamic graphs[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, the 32nd Innovative Applications of Artificial Intelligence Conference, the 10th AAAI Sym-posium on Educational Advances in Artificial Intelligence, New York, Feb 7-12, 2020. Menlo Park: AAAI, 2020: 5363-5370.
[155] 赵振兵, 段记坤, 孔英会, 等. 基于门控图神经网络的栓母对知识图谱构建与应用[J]. 电网技术, 2021, 45(1): 98-106.
ZHAO Z B, DUAN J K, KONG Y H, et al. Construction and application of bolt and nut pair knowledge graph based on GGNN[J]. Power System Technology, 2021, 45(1): 98-106.
[156] WANG Z, CHEN T, REN J, et al. Deep reasoning with knowledge graph for social relationship understanding[J]. arXiv:1807.00504, 2018.
[157] WU S, TANG Y, ZHU Y, et al. Session-based recommen-dation with graph neural networks[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Confe-rence, the 9th AAAI Symposium on Educational Advan-ces in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 346-353.