计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 74-85.doi: 10.11896/jsjkx.210100122
马瑞新, 李泽阳, 陈志奎, 赵亮
MA Rui-xin, LI Ze-yang, CHEN Zhi-kui, ZHAO Liang
摘要: 近年来,随着互联网技术以及引用模式的快速发展,计算机世界的数据规模呈指数型增长,这些数据中蕴含着大量有价值的信息,如何从中筛选出知识并将这些知识进行有效组织和表达引起了广泛关注。知识图谱由此而生,面向知识图谱的知识推理就是知识图谱研究的热点之一,已经在语义搜索、智能问答等领域取得了重大成就。然而,由于样本数据存在各种缺陷,例如样本数据缺少头尾实体、查询路径过长、样本数据错误等,因此面对上述特点的零样本、单样本、少样本和多样本知识图谱推理更受瞩目。文中将从知识图谱的基本概念和基础知识出发,介绍近年来知识图谱推理方法的最新研究进展。具体而言,根据样本数据量大小的不同,将知识图谱推理方法分为多样本推理、少样本推理和零与单样本推理。模型使用超过5个实例数进行推理的为多样本推理,模型使用2~5实例数进行推理的为少样本推理,模型使用零个或者一个实例数进行推理的为零与单样本推理。根据方法的不同,将多样本知识图谱推理细分为基于规则的推理、基于分布式的推理、基于神经网络的推理以及基于其他的推理,将少样本知识图谱推理细分为基于元学习的推理与基于相邻实体信息的推理,具体分析总结这些方法。此外,进一步讲述了知识图谱推理的典型应用,并探讨了知识图谱推理现存的问题、未来的研究方向和前景。
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[1] GUAN SP,JIN X L,JIA Y T,et al.Knowledge reasoning over knowledge graph:A survey[J].Journal of Software,2018,29(10):2966-2994. [2] AUER S,BIZER C,KOBILAROV G,et al.Dbpedia:A nucleus for a web of open data[M]//The Semantic Web.Berlin:Springer,2007:722-735. [3] BOLLACKER K,EVANS C,PARITOSH P,et al.Freebase:acollaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data.2008:1247-1250. [4] CARLSON A,BETTERIDGE J,KISIEL B,et al.Toward an architecture for never-ending language learning[C]//Twenty-Fourth AAAI Conference on Artificial Intelligence.2010:1306-1313. [5] WU W,LI H,WANG H,et al.Probase:A probabilistic taxonomy for text understanding[C]//Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data.2012:481-492. [6] 自底向上构建知识图谱全过程[OL].[2018-07-03].https://zhuanlan.zhihu.com/p/38891715. [7] MILLER G A.WordNet:An electronic lexical database[M].MIT press,1998. [8] YOU B,LIU X R,LI N,et al.Using information content to evaluate semantic similarity on HowNet[C]//2012 Eighth International Conference on Computational Intelligence and Security.IEEE,2012:142-145. [9] ERXLEBEN F,GÜNTHER M,KRÖTZSCH M,et al.Introducing Wikidata to the linked data web[C]//International Semantic Web Conference.Cham:Springer,2014:50-65. [10] HUANG H Q,YU J,LIAO X,et al.Review on KnowledgeGraphs.Computer Systems & Applications,2019,28(6):1-12. [11] CARLSON A,BETTERIDGE J,KISIELB,et al.Toward an architecture for never-ending language learning[C]//Twenty-Fourth AAAI Conference on Artificial Intelligence.2010:1306-1313. [12] 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 & Knowledge Management.2013:2129-2138. [13] COHEN W W.Tensorlog:A differentiable deductive database[J].arXiv:1605.06523,2016. [14] PAULHEIM H,BIZER C.Improving the quality of linked data using statistical distributions[J].International Journal on Semantic Web and Information Systems,2014,10(2):63-86. [15] JANG S,MEGAWATI M,CHOI J,et al.Semi-Automatic Qua-lity Assessment of Linked Data without Requiring Ontology[C]//NLP-DBPEDIA@ ISWC.2015:45-55. [16] LAO N,COHEN W W.Relational retrieval using a combination of path-constrained random walks[J].Machine Learning,2010,81(1):53-67. [17] LAO N,MITCHELL T,COHEN W.Random walk inference and learning in a large scale knowledge base[C]//Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing.2011:529-539. [18] ZHAO Z,JIA Y,WANG Y.Content-structural relation infer-ence in knowledge base[C]//Twenty-Eighth AAAI Conference on Artificial Intelligence.2014:3154-3155. [19] KOTNIS B,BANSAL P,TALUKDAR P.Knowledge base inference using bridging entities[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Proces-sing.2015:2038-2043. [20] GALÁRRAGA L A,TEFLIOUDI C,HOSE K,et al.AMIE:association rule mining under incomplete evidence in ontological knowledge bases[C]//Proceedings of the 22nd International Conference on World Wide Web.2013:413-422. [21] GALÁRRAGA L,TEFLIOUDI C,HOSEK,et al.Fast rulemining in ontological knowledge bases with AMIE $$+$$+[J].The International Journal on Very Large Data Bases,2015,24(6):707-730. [22] GARDNER M,MITCHELL T.Efficient and expressive knowledge base completion using subgraph feature extraction[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1488-1498. [23] LIU Q,JIANG L,HAN M,et al.Hierarchical random walk inference in knowledge graphs[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval.2016:445-454. [24] BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[J].Advances in Neural Information Processing Systems,2013,26:2787-2795. [25] WANG Z,ZHANG J,FENG J,et al.Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence.2014:1112-1119. [26] WEN J,LI J,MAO Y,et al.On the Representation and Embedding of Knowledge Bases beyond Binary Relations[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.2016:1300-1307. [27] JIA Y,WANG Y,LIN H,et al.Locally adaptive translation for knowledge graph embedding[C]//Thirtieth AAAI Conference on Artificial Intelligence.2016:992-998. [28] XIAO H,HUANG M,HAO Y,et al.TransG:A generativemixture model for knowledge graph embedding[J].arXiv:1509.05488,2015. [29] LIN Y,LIU Z,SUN M,et al.Learning entity and relation embeddings for knowledge graph completion[C]//Twenty-ninth AAAI Conference on Artificial Intelligence.2015:2181-2187. [30] 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(Long Papers).2015:687-696. [31] FAN M,ZHOU Q,CHANG E,et al.Transition-based know-ledge graph embedding with relational mapping properties[C]//Proceedings of the 28th Pacific Asia Conference on Language,Information and Computing.2014:328-337. [32] JI G,LIU K,HE S,et al.Knowledge graph completion witha daptive sparse transfer matrix[C]//Thirtieth AAAI Confe-rence on Artificial Intelligence.2016:985-991. [33] GUO S,WANG Q,WANG B,et al.Semantically smooth know-ledge graph embedding[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing(Long Papers).2015:84-94. [34] NGUYEN D Q,SIRTS K,QU L,et al.Neighborhood mixture model for knowledge base completion[C]//Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning.2016:40-50. [35] NICKEL M,TRESP V,KRIEGELH P.A three-way model for collective learning on multi-relational data[C]//Proceedings of the 28th Int'l Conf.on Machine Learning.New York:ACM Press,2011:809-816. [36] CHANG K W,YIH W,YANG B,et al.Typed tensor decomposition of knowledge bases for relation extraction[C]//Procee-dings of the 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP).2014:1568-1579. [37] NICKEL M,JIANG X,TRESP V.Reducing the Rank in Relational Factorization Models by Including Observable Patterns[C]//Advances in Neural Information Processing Systems 27:Annual Conference on Neural Information Processing Systems.2014:1179-1187. [38] TROUILLON T,WELBL J,RIEDEL S,et al.Complex embeddings for simple link prediction[C]//International Conference on Machine Learning.PMLR,2016:2071-2080. [39] TAY Y,LUU A T,HUI S C,et al.Random semantic tensor ensemble for scalable knowledge graph link prediction[C]//Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.2017:751-760. [40] HE S,LIU K,JI G,et al.Learning to represent knowledgegraphs with gaussian embedding[C]//Proceedings of the 24th ACM International on Conference on Information and Know-ledge Management.2015:623-632. [41] XIAO H,HUANG M,ZHU X.From one point to a manifold:Knowledge graph embedding for precise link prediction[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.2016:1315-1321. [42] LIN Y,LIU Z,LUAN H,et al.Modeling relation paths for representation learning of knowledge bases[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:705-714. [43] LIN X,LIANG Y,GIUNCHIGLIA F,et al.CompositionalLearning of Relation Path Embedding for Knowledge Base Completion[J].arXiv:1611.07232,2016. [44] FENG J,HUANG M,YANG Y,et al.GAKE:Graph awareknowledge embedding[C]//Proceedings of COLING 2016,the 26th International Conference on Computational Linguistics:Technical Papers.2016:641-651. [45] WANG Z,LI L,ZENG D D,et al.Attention-based multi-hopreasoning for knowledge graph[C]//2018 IEEE International Conference on Intelligence and Security Informatics(ISI).IEEE,2018:211-213. [46] CHEN H,LI G,SUN Y,et al.A multi-hop link prediction approach based on reinforcement learning in knowledge graphs[C]//2018 11th International Symposium on Computational Intelligence and Design(ISCID).IEEE,2018:165-169. [47] SOCHER R,CHEN D,MANNING C D,et al.Reasoning with neural tensor networks for knowledge base completion[C]//Advances in Neural Information Processing Systems,2013:926-934. [48] CHEN D,SOCHER R,MANNING C D,et al.Learning newfacts from knowledge bases with neural tensor networks and semantic word vectors[J].arXiv:1301.3618,2013. [49] SHI B,WENINGER T.ProjE:Embedding projection for knowledge graph completion[C]//Proceedings of the AAAI Confe-rence on Artificial Intelligence.2017:1236-1242. [50] NGUYEN D Q,NGUYEN T D,NGUYEN D Q,et al.A novel embedding model for knowledge base completion based on con-volutional neural network[C]//Proceedings of the 2018 Confe-rence of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2018:327-333. [51] NEELAKANTAN A,ROTH B,MCCALLUM A.Compositional vector space models for knowledge base completion[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing.2015:156-166. [52] DAS R,NEELAKANTAN A,BELANGER D,et al.Chains of reasoning over entities,relations,and text using recurrent neural networks[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics.2017:132-141. [53] GRAVES A,WAYNE G,REYNOLDS M,et al.Hybrid computing using a neural network with dynamic external memory[J].Nature,2016,538(7626):471-476. [54] WANG Q,YIN H,WANG W,et al.Multi-hop path queries overknowledge graphs with neural memory networks[C]//International Conference on Database Systems for Advanced Applications.Cham:Springer,2019:777-794. [55] HAN X,SUN L.Context-Sensitive inference rule discovery:Agraph-based method[C]//Proceedings of COLING 2016,the 26th International Conference on Computational Linguistics:Technical Papers.2016:2902-2911. [56] WANG W Y,COHEN W W.Learning First-Order Logic Embeddings via Matrix Factorization[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.2016:2132-2138. [57] GUO S,WANG Q,WANG L,et al.Jointly embedding know-ledge graphs and logical rules[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Proces-sing.2016:192-202. [58] SCHLICHTKRULL M,KIPF T N,BLOEM P,et al.Modeling relational data with graph convolutional networks[C]//Euro-pean Semantic Web Conference.Cham:Springer,2018:593-607. [59] YANG F,YANG Z,COHEN W W.Differentiable learning oflogical rules for knowledge base reasoning[C]//Annual Confe-rence on Neural Information Processing Systems.2017:2319-2328. [60] XIONG W,YU M,CHANG S,et al.One-shot relational lear-ning for knowledge graphs[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.2018:1980-1990. [61] CHEN M,ZHANG W,ZHANG W,et al.Meta relational lear-ning for few-shot link prediction in knowledge graphs[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Confe-rence on Natural Language Processing.2019:4216-4225. [62] LV X,GU Y,HAN X,et al.Adapting meta knowledge graph information for multi-hop reasoning over few-shot relations[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing.2019:3374-3379. [63] LIN X V,SOCHER R,XIONG C.Multi-hop knowledge graph reasoning with reward shaping[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Proces-sing.2018:3243-3253. [64] FINN C,ABBEEL P,LEVINE S.Model-agnostic meta-learning for fast adaptation of deep networks[C]//International Confe-rence on Machine Learning.PMLR,2017:1126-1135. [65] WANG H,XIONG W,YU M,et al.Meta reasoning over know-ledge graphs[J].arXiv:1908.04877,2019. [66] ZHANG C,YAO H,HUANG C,et al.Few-shot knowledgegraph completion[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:3041-3048. [67] YIN W,YAGHOOBZADEH Y,SCHÜTZE H.Recurrent one-hop predictions for reasoning over knowledge graphs[C]//Proceedings of the 27th International Conference on Computational Linguistics.2018:2369-2378. [68] SHENG J,GUO S,CHEN Z,et al.Adaptive Attentional Network for Few-Shot Knowledge Graph Completion[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing.2020:1681-1691. [69] DU Z,ZHOU C,DING M,et al.Cognitive knowledge graph reasoning for one-shot relational learning[J].arXiv:1906.05489,2019. [70] MIRTAHERI M,ROSTAMI M,REN X,et al.One-shot Lear-ning for Temporal Knowledge Graphs[J].arXiv:2010.12144,2020. |
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