计算机科学与探索 ›› 2023, Vol. 17 ›› Issue (1): 27-52.DOI: 10.3778/j.issn.1673-9418.2207060
孙水发,李小龙,李伟生,雷大江,李思慧,杨柳,吴义熔
出版日期:
2023-01-01
发布日期:
2023-01-01
SUN Shuifa, LI Xiaolong, LI Weisheng, LEI Dajiang, LI Sihui, YANG Liu, WU Yirong
Online:
2023-01-01
Published:
2023-01-01
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。
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.
[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. |
[1] | 马力, 姚伟凡. 结合关系路径与有向子图推理的链接预测方法[J]. 计算机科学与探索, 2023, 17(2): 478-488. |
[2] | 尹华, 肖石冉, 陈智全, 胡振生, 龙泳潮. 多语义关系嵌入的知识图谱补全方法[J]. 计算机科学与探索, 2023, 17(2): 467-477. |
[3] | 高仰, 刘渊. 融合社交关系和知识图谱的推荐算法[J]. 计算机科学与探索, 2023, 17(1): 238-250. |
[4] | 于慧琳, 陈炜, 王琪, 高建伟, 万怀宇. 使用子图推理实现知识图谱关系预测[J]. 计算机科学与探索, 2022, 16(8): 1800-1808. |
[5] | 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. |
[6] | 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. |
[7] | 韩毅, 乔林波, 李东升, 廖湘科. 知识增强型预训练语言模型综述[J]. 计算机科学与探索, 2022, 16(7): 1439-1461. |
[8] | 郭晓旺, 夏鸿斌, 刘渊. 融合知识图谱与图卷积网络的混合推荐模型[J]. 计算机科学与探索, 2022, 16(6): 1343-1353. |
[9] | 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. |
[10] | 王宝亮, 潘文采. 基于知识图谱的双端邻居信息融合推荐算法[J]. 计算机科学与探索, 2022, 16(6): 1354-1361. |
[11] | 张子辰, 岳昆, 祁志卫, 段亮. 时序知识图谱的增量构建[J]. 计算机科学与探索, 2022, 16(3): 598-607. |
[12] | 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. |
[13] | 吴静, 谢辉, 姜火文. 图神经网络推荐系统综述[J]. 计算机科学与探索, 2022, 16(10): 2249-2263. |
[14] | 李想, 杨兴耀, 于炯, 钱育蓉, 郑捷. 基于知识图谱卷积网络的双端推荐算法[J]. 计算机科学与探索, 2022, 16(1): 176-184. |
[15] | 袁立宁, 李欣, 王晓冬, 刘钊. 图嵌入模型综述[J]. 计算机科学与探索, 2022, 16(1): 59-87. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||