知识图谱推理：现代的方法与应用

doi:10.11959/j.issn.2096-0271.2021025

摘要/Abstract

摘要：

知识图谱推理技术旨在根据已有的知识推导出新的知识，是使机器智能具有和人类一样的推理和决策能力的关键技术之一。系统地研究了知识图谱推理的现代方法，以统一的框架介绍了向量空间中进行知识图谱推理的模型，包括基于几何运算嵌入欧几里得空间和双曲空间的方法，基于卷积神经网络、胶囊网络、图神经网络等深度网络模型的方法。同时，系统地梳理了知识推理技术在各技术领域和各行业的应用情况，指出了当前存在的挑战以及其中蕴含的机会。

关键词: 知识推理, 双曲空间嵌入, 几何运算, 胶囊网络, 图神经网络

Abstract:

Knowledge reasoning over knowledge graph aims to discover new knowledge according to the existing knowledge.It is a pivotal technology to realize the human reasoning and decision-making ability of machine.The modern methods of knowledge reasoning over knowledge graph were studied systematically.And the methods based on vector representations with a unified framework were introduced, including the methods based on embedding into Euclidean space and hyperbolic space, and based on deep learning methods such as convolution neural network, capsule network, graph neural network, etc.Simultaneously, the applications of knowledge reasoning in various technical fields and industries were presented, and the existing challenges and opportunities were pointed out as well.

Key words: knowledge reasoning, hyperbolic space embedding, geometric operation, capsule network, graph neural network

中图分类号:

TP391.1

王文广. 知识图谱推理：现代的方法与应用[J]. 大数据, 2021, 7(3): 42-59.

Wenguang WANG. Knowledge graph reasoning: modern methods and applications[J]. Big Data Research, 2021, 7(3): 42-59.

图/表 10

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

参考文献 56

[1]	吴运兵, 杨帆, 赖国华 ,等. 知识图谱学习和推理研究进展[J]. 小型微型计算机系统, 2016,37(9): 2007-2013.
	WU Y B , YANG F , LAI G H ,et al. Research progress of knowledge graph learning and reasoning[J]. Journal of Chinese Computer Systems, 2016,37(9): 2007-2013.
[2]	刘知远, 孙茂松, 林衍凯 ,等. 知识表示学习研究进展[J]. 计算机研究与发展, 2016,53(2): 247-261.
	LIU Z Y , SUN M S , LIN Y K ,et al. Knowledge representation learning:a review[J]. Journal of Comp uter Research and Development, 2016,53(2): 247-261.
[3]	MITCHELL T , COHEN W , HRUSCHKA E ,et al. Never-ending learning[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2015: 2302-2310.
[4]	SCHOENMACKERS S , DAVIS J , ETZIONI O ,et al. Learning first-order horn clauses from web text[C]// Proceedings of the 2010 Conference on Empirical Methods in Natura l Language Processing.[S.l.]: Association for Computational Linguistics, 2010: 1088-1098.
[5]	KOK S , DOMINGOS P . Learning the structure of Markov logic networks[C]// Proceedings of the 22nd International Conference on Machine Learning (ICML 2005). New York :ACM Press, 2005: 441-448.
[6]	RICHARDSON M , DOMINGOS P . Markov logic networks[J]. Machine Learning, 2006,62(1-2): 107-136.
[7]	OND?EJ K , JESSE D . Markov logic networks for knowledge base compl etion:a theoretical analysis under the MCAR assumption[C]// Proceedings of the 35th Uncertainty in Artificial Intelligence Conference.[S.l.:s.n.], 2020: 1138-1148.
[8]	CHEN X , CHEN H , ZHANG N ,et al. OWL reasoning over big biomedical data[C]// Proceedings of the 2013 IEEE International Conference on Big Data. Piscataway:IEEE Pr ess, 2013: 29-36.
[9]	LAO N , COHEN W . Relational retrieval using a combination of path-constrained random walks[J]. Machine Learning, 2010,81(1): 53-67.
[10]	XIONG W H , HOANG T , WANG W Y . DeepPath:a reinforcement learning method for knowledge graph reasoning[C]// Proceedings of the 2017 Conference on Empirical Method s in Natural Language Processing.[S.l.:s.n.], 2017.
[11]	BORDES A , USUNIER N , GARCíADURáN A ,et al. Translating embeddings for modeling multi-relational data[C]// Proceedings of the 26th International Conference on Ne ural Information Processing Systems.Red Hook:Curran Associates Inc. , 2013: 2787-2795.
[12]	MIKOLOV T , CORRADO G , CHEN K ,et al. Efficient estimation of word representations in vector space[C]// Proceedings of the International Conference on Learning Re presentations (ICLR 2013).[S.l.:s.n.], 2013.
[13]	WANG Z , ZHANG J W , FENG J L ,et al. Knowledge graph embedding by translating on hyperplanes[J]. Proceedings of the 28th AAAI Conference on Artificial Intelligenc e.Palo Alto:AAAI Press, 2014: 1112-1119.
[14]	SUN Z , DENG Z H , NIE J Y ,et al. RotatE:knowledge graph embedding by relational rotation in complex space[C]// Proceedings of the 7th International Conference o n Learning Representations.[S.l:s.n.], 2019.
[15]	LIN Y K , LIU Z Y , SUN M S ,et al. Learning entity and relation embeddings for knowledge graph completion[C]// Proceedings of the 29th AAAI Conference on Artifici al Intelligence. Palo Alto:AAAI Press, 2015: 2181-2187.
[16]	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 Li nguistics and the 7th International Joint Conference on Natural Language Processing.[S.l.]: Association for Computational Linguistics, 2015: 687-696.
[17]	XIAO H , HUANG M L , ZHU X Y . TransG:a generative model for knowledge graph embedding[C]// Proceedings of the 54th Annual Meeting of the Association for Computati onal Linguistics.[S.l:s.n.], 2016: 2316-2325.
[18]	GRIFFITHS T L , GHAHRAMANI Z B . The Indian buffet process:an introduction and review[J]. Journal of Machine Learning Research, 2011,12(2): 1185-1224.
[19]	SARKA R R , . Low distortion delaunay embedding of trees in hyperbolic plane[C]// Proceedings of the 19th International Symposium on Graph Drawing.[S.l:s.n.], 2011: 355-366.
[20]	OCTAVIAN G , GARY B , THOMAS H . Hyperbolic neural networks[C]// Advances in Neural Information Processing Systems.[S.l:s.n.], 2018.
[21]	CHAMI I , YING R,Ré C , et al . Hyperbolic graph convolutional neural networks[C]// Advances in Neural Information Processing Systems.[S.l:s.n.], 2019: 4869-4880.
[22]	LIU Q , NICKEL M , KIELA D . Hyperbolic graph neural networks[C]// Advances in Neural Information Processing Systems.[S.l:s.n.], 2019.
[23]	BALAEVI I , ALLEN C , HOS PEDALES T . Multi-relational Poincaré graph embeddings[C]// Advances in Neural Information Processing Systems.[S.l:s.n.], 2019.
[24]	CHAMI I , WOLF A , JUAN D C ,et al. Low-dimensional hyperbolic knowledge graph embeddings[C]// Proceedings of the 58th Annual Meeting of the Association for Comput ational Linguistics.[S.l:s.n.], 2020: 6901-6914.
[25]	DETTMERS T , MINERVINI P , STENETORP P ,et al. Convolutional 2D knowledge graph embeddings[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2018.
[26]	JIANG X T , WANG Q , WANG B . Adaptive convolution for multi-relational learning[C]// Proceedings of the 2019 Conference of the North American Chapter of the Associ ation for Computational Linguistics:Human Language Technologies.[S.l. ]:Association for Computational Linguistics, 2019: 978-987.
[27]	NGUYEN Q , VU T , NGUYEN D ,et al. A capsule network-based embedding model for knowledge graph completion and search personalization[C]// Proceedings of the 2019 C onference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.[S.l. ]:Association for Computational Linguistics, 2019: 2180-2189.
[28]	SABOUR S , FROSST N , HINTON G E . Dynamic routing between capsules[C]// Advances in Neural Information Processing Systems.[S.l:s.n.], 2017.
[29]	SCHLICHTKRULL M , KIPF T , BLOEM P ,et al. Modeling relational data with graph convolutional networks[C]// Proceedings of the 2018 European Semantic Web Conference . Heidelberg:Springer, 2018: 593-607.
[30]	YANG B , YIH S , HE X ,et al. Embedding entities and relations for learning and inference in knowledge bases[C]// Proceedings of the 3rd International Conference o n Learning Representations (ICLR 2015).[S.l.:s.n.], 2015.
[31]	ZHANG Z , ZHUANG F , ZHU H ,et al. Relational graph neural network with hierarchical attention for knowledge graph completion[C]// Proceedings of the 34th AAAI Con ference on Artificial Intelligence. Palo Alto:AAAI Press, 2020: 9612-9619.
[32]	王文广, 徐永林, 贺梦洁 ,等. 基于知识图谱的通用知识问答系统:体系与方法[J]. 新一代信息技术, 2020,3(7): 38-47.
	WANG W G , XU Y L , HE M J ,et al. Knowledge graph based universal question answering syst em:framework and methods[J]. New Generation of Information Technology, 2020,3(7): 38-47.
[33]	邹艳珍, 王敏, 谢冰 ,等. 基于大数据的软件项目知识图谱构造及问答方法[J]. 大数据, 2021,7(1): 22-36.
	ZOU Y Z , WANG M , XIE B ,et al. Software knowledge graph construction and Q＆A technology based o n big data[J]. Big Data Research, 2021,7(1): 22-36.
[34]	SAXENA A , TRIPATHI A , TALUKDAR P . Improving multi-hop question answering over knowledge graphs using knowledge base embeddings[C]// Proceedings of the 58th Annua l Meeting of the Association for Computational Linguistics.[S.l:s.n.], 2020: 4498-4507.
[35]	WANG X , ZHAO S , CHENG B ,et al. HGMAN:multi-hop and multi-answer question answering based on heterogeneous knowledge graph (student abstract)[C]// Proceedings o f the 34th AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2020: 13953-13954.
[36]	LIU J , SUI D , LIU K ,et al. Graph-based knowledge integration for question answering over dialogue[C]// Proceedings of the 28th International Conference on Compu tational Linguistics.[S.l:s.n.], 2020: 2425-2435.
[37]	HUANG X , ZHANG J , LI D ,et al. Knowledge graph embedding based question answering[C]// Proceedings of the 12th ACM International Conference on Web Search and Dat a.[S.l:s.n.], 2019: 205-113.
[38]	CHEN Q , LIN J , ZHANG Y ,et al. Towards knowledge-based recommender dialog system[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing.[S.l]: Association for Computational Linguistics, 2019.
[39]	ZHANG F , YUAN J , LIAN D ,et al. Collaborative knowledge base embedding for recommender systems[C]// Proceedings of the 22nd ACM SIGKDD International Conference o n Knowledge Discovery and Data Mining. New York:ACM Press, 2016: 353-362.
[40]	WANG X , HE X , CAO Y ,et al. KGAT:knowledge graph attention network for recommendation[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowle dge Discovery ＆Data Mining. New York:ACM Press, 2019: 950-958.
[41]	VU T , NGUYEN D , JOHNSON M ,et al. Search personalization with embeddings[C]// Proceedings of the 39th European Conference on Information Retrieval. Heidelberg:S pringer, 2017.
[42]	NGUYEN D Q , NGUYEN T D , NGUYEN D Q ,et al. A convolutional neural network-based model for knowledge base completion and its application to search personalization[J]. Semantic Web, 2018,10(4): 1-14.
[43]	臧根林, 王亚强, 吴庆蓉 ,等. 智慧城市知识图谱模型与本体构建方法[J]. 大数据, 2020,6(2): 96-106.
	ZANG G L , WANG Y Q , WU Q R ,et al. Model and construction method of the ontology of knowle dge graph of smart city[J]. Big Data Research, 2020,6(2): 96-106.
[44]	DING X , ZHANG Y , LIU T ,et al. Knowledge-driven event embedding for stock prediction[C]// Proceedings of the 26th International Conference on Computational Lingu istics:Technical Papers.[S.l.:s.n.], 2016: 2133-2142.
[45]	金磐石, 万光明, 沈丽忠 . 基于知识图谱的小微企业贷款申请反欺诈方案[J]. 大数据, 2019,5(4): 100-112.
	JIN P S , WAN G M , SHEN L Z . Knowledge graph-based fraud detection for small and micro enterpri se loans[J]. Big Data Research, 2019,5(4): 100-112.
[46]	ZHENG S , RAO J , SONG Y ,et al. PharmKG:a dedicated knowledge graph benchmark for bomedical data mining[J]. Briefings in Bioinformatics, 2020.
[47]	WISHART D , FE UNANG Y , GUO A ,et al. DrugBank 5.0:a major update to the DrugBank database for 2018[J]. Nucleic Acids Research, 2018,46(D1): 1074-1082.
[48]	VE LIKOVI P , CUCURULL G , CASANOVA A ,et al. Graph attention networks[C]// Proceedings of the 6th International Conference on Learning Representations.[S.l.:s.n.], 2018.
[49]	MARINKA Z , MONICA A , JURE L . Modeling polypharmacy side effects with graph convolutional networks[J]. Bioinformatics, 2018,34(13): 457-466.
[50]	SANG S , YANG Z , LIU X ,et al. GrEDeL:a knowledge graph embedding based method for drug discovery from biomedical literatures[J]. IEEE Access, 2018,7: 8404-8415.
[51]	HE L , JIANG P . Manufacturing knowledge graph:a connectivism to answer production problems query with knowledge reuse[J]. IEEE Access, 2019,7: 101231-101244.
[52]	BADER S , GRANGEL-GONZALEZ , NANJAPPA P ,et al. A knowledge graph for industry 4.0[C]// Proceedings of the 2020 European Semantic Web Conference. Heidelberg:Sprin ger, 2020: 465-480.
[53]	GAROFALO M , PELLEGRINO M , ALTABBA A ,et al. Leveraging knowledge graph embedding techniques for industry 4.0 use cases[J]. arXiv preprint, 2018,arXiv:1808.00434.
[54]	RINGSQUANDL M , LAMPARTER S , LEPRATTI R ,et al. Knowledge fusion of manufacturing operations data using representation learning[C]// Proceedings of the 2017 IFIP International Conference on Advances in Production Management Systems. Heidelberg:Springer, 2017: 302-310.
[55]	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.[S.l.:s.n.], 2020: 119-126.
[56]	VASWANI A , SHAZEER N , PARMAR N ,et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems.Red Hook:Curran Associates Inc. , 2017: 6000-6010.