大数据 ›› 2021, Vol. 7 ›› Issue (3): 42-59.doi: 10.11959/j.issn.2096-0271.2021025
所属专题: 知识图谱
王文广
出版日期:
2021-05-15
发布日期:
2021-05-01
作者简介:
王文广(1984- ),男,达而观信息科技(上海)有限公司高级工程师、副总裁,中国计算机学会会员、中国中文信息学会语言与知识计算专业委员会委员、中国人工智能学会深度学习专业委员会委员,主要研究方向为知识图谱、自然语言处理、计算机视觉、深度学习、深度强化学习等。
Wenguang WANG
Online:
2021-05-15
Published:
2021-05-01
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。
中图分类号:
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.
[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. |
[1] | 于亚秀, 李欣. 数字人文视域中的古籍文本标注方法研究——以MARKUS为例[J]. 大数据, 2022, 8(6): 15-25. |
[2] | 崔雨萌, 王靖亚, 闫尚义, 陶知众. 基于深度学习的警情记录关键信息自动抽取[J]. 大数据, 2022, 8(6): 127-142. |
[3] | 韩立帆, 季紫荆, 陈子睿, 王鑫. 数字人文视域下面向历史古籍的信息抽取方法研究[J]. 大数据, 2022, 8(6): 26-39. |
[4] | 关海山, 郑玉龙, 魏笔凡, 张泽民, 岳浩, 师斌, 董博. 税收优惠政策关键要素抽取与可视化分析[J]. 大数据, 2022, 8(5): 106-123. |
[5] | 徐康庭, 宋威. 结合语言知识和深度学习的中文文本情感分析方法[J]. 大数据, 2022, 8(3): 115-127. |
[6] | 肖晓霞, 刘明婷, 杨冯天赐, 刘鉴建县, 杨阳, 石月. 基于NLP的中医医案文本快速结构化方法[J]. 大数据, 2022, 8(3): 128-139. |
[7] | 黄辉, 秦永彬, 陈艳平, 黄瑞章. 基于BERT阅读理解框架的司法要素抽取方法[J]. 大数据, 2021, 7(6): 19-29. |
[8] | 孙倩, 秦永彬, 黄瑞章, 刘丽娟, 陈艳平. 结合案件要素序列的罪名预测方法[J]. 大数据, 2021, 7(6): 30-40. |
[9] | 麻珂欣, 魏笔凡, 马杰, 刘均, 黄毅, 胡珉, 冯俊兰. 知识主题间先序关系挖掘[J]. 大数据, 2020, 6(6): 26-39. |
[10] | 肖时耀, 吕慰, 陈洒然, 秦烁, 黄格, 蔡梦思, 谭跃进, 谭旭, 吕欣. 基于百度贴吧的HIV高危人群特征分析[J]. 大数据, 2019, 5(1): 98-108. |
[11] | 杜小勇, 陈跃国. 大数据的价值发现方法[J]. 大数据, 2017, 3(2): 19-25. |
[12] | 赵妍妍,秦兵,刘挺. 社会焦点透视镜系统—— 大数据视角下的舆情观测平台[J]. 大数据, 2016, 2(2): 46-55. |
[13] | 程学旗, 兰艳艳. 网络大数据的文本内容分析[J]. 大数据, 2015, 1(3): 62-71. |
[14] | 袁书寒, 向阳, 鄂世嘉. 基于特征学习的文本大数据内容理解及其发展趋势[J]. 大数据, 2015, 1(3): 72-81. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||
|