Computer Science ›› 2023, Vol. 50 ›› Issue (4): 149-158.doi: 10.11896/jsjkx.211200175
• Artificial Intelligence • Previous Articles Next Articles
SHEN Qiuhui1, ZHANG Hongjun2, XU Youwei1, WANG Hang1, CHENG Kai2
CLC Number:
[1]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[C]//Neural Information Processing Systems(NIPS).2013:1-9. [2]WANG Z,ZHANG J,FENG J,et al.Knowledge graph embedding by translating on hyperplanes[C]//AAAI Conference on Artificial Intelligence.2014:1112-1119. [3]SUN Z,VASHISHTH S,SANYAL S,et al.A Re-evaluation of Knowledge Graph Completion Methods[EB/OL].(2020-07-08)[2021-12-13].https://arxiv.org/abs/1911.03903v3. [4]NICKEL M,TRESP V,KRIEGEL H P.A three-way model for collective learning on multi-relational data[C]//International Conference on International Conference on Machine Learning.2011:809-816. [5]HAYASHI K,SHIMBO M.On the Equivalence of Holographicand Complex Embeddings for Link Prediction[C]//Proceedings of the 55th Annual Meeting of the Association for Computa-tional Linguistics(ACL).2017:554-559. [6]NICKEL M,ROSASCO L,POGGIO T.Holographic embed-dings of knowledge graphs[C]//AAAI Conference on Artificial Intelligence.2016:1955-1961. [7]TROUILLON T,WELBL J,RIEDEL S,et al.Complex embeddings for simple link prediction[C]//International Conference on Machine Learning.PMLR,2016:2071-2080. [8]TROUILLON T,NICKEL M.Complex and Holographic Em-beddings of Knowledge Graphs:A Comparison[EB/OL].(2017-07-23)[2021-12-13].https://arxiv.org/abs/1707.01475. [9]BORDES A,GLOROT X,WESTON J,et al.Joint learning ofwords and meaning representations for open-text semantic parsing[C]//Artificial Intelligence and Statistics.PMLR,2012:127-135. [10]BORDES A,WESTON J,COLLOBERT R,et al.Learningstructured embeddings of knowledge bases[C]//AAAI Confe-rence on Artificial Intelligence.2011:301-306. [11]GARCÍA-DURÁN A,BORDES A,USUNIER N.Effectiveblending of two and three-way interactions for modeling multi-relational data[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.2014:434-449. [12]YANG B,YIH W,HE X,et al.Embedding entities and relations for learning and inference in knowledge bases[C]//International Conference on Learning Representations(ICLR).2015:1-13. [13]FENG J,HUANG M,WANG M,et al.Knowledge graph em-bedding by flexible translation[C]//International Conference on Principles of Knowledge Representation and Reasoning.2016:557-560. [14]LIN Y,LIU Z,SUN M,et al.Learning entity and relation embeddings for knowledge graph completion[C]//AAAI Confe-rence on Artificial Intelligence.2015:2181-2187. [15]XIE R,LIU Z,SUN M.Representation learning of knowledgegraphs with hierarchical types[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.2016:2965-2971. [16]XIE R,LIU Z,JIA J,et al.Representation learning of knowledge graphs with entity descriptions[C]//AAAI Conference on Artificial Intelligence.2016:2659-2665. [17]XIE R,LIU Z,LUAN H,et al.Image-embodied knowledge representation learning[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.2017:3140-3146. [18]DASGUPTA S,RAY S N,TALUKDAR P.Hyte:Hyperplanebased temporally aware knowledge graph embedding[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing(EMNLP).2018:2001-2011. [19]JI G,HE S,XU L,et al.Knowledge graph embedding via dy-namic 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.2015:687-696. [20]JI G,LIU K,HE S,et al.Knowledge graph completion withadaptive sparse transfer matrix[C]//AAAI Conference on Artificial Intelligence.2016:985-991. [21]XIE Q,MA X,DAI Z,et al.An Interpretable Knowledge Transfer Model for Knowledge Base Completion[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.2017:950-962. [22]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. [23]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. [24]NGUYEN D Q,SIRTS K,QU L,et al.STransE:a novel embedding model of entities and relationships in knowledge bases[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2016:460-466. [25]LIN Y,LIU Z,LUAN H,et al.Modeling Relation Paths forRepresentation Learning of Knowledge Bases[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:705-714. [26]FAN M,ZHOU Q,CHANG E,et al.Transition-based know-ledge graph embedding with relational mapping properties[C]//Proceedings of the 28th Pacific AsiaConference on Language,Information and Computing.2014:328-337. [27]EBISU T,ICHISE R.Toruse:Knowledge graph embedding on a lie group[C]//AAAI Conference on Artificial Intelligence.2018:1819-1826. [28]LEBLAYJ,CHEKOL M W.Deriving validity time in knowledge graph[C]//Proceedings of the 26th International World Wide Web Conference.2018:1771-1776. [29]XIAO H,HUANG M,HAO Y,et al.TransA:An adaptive approach for knowledge graph embedding[EB/OL].(2015-09-28)[2021-12-13].https://arxiv.org/abs/1509.05490. [30]QIAN W,FU C,ZHU Y,et al.Translating embeddings forknowledge graph completion with relation attention mechanism[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence.2018:4286-4292. [31]SOCHER R,CHEN D,MANNING C D,et al.Reasoning with neural tensor networks for knowledge base completion[C]//International Conference on Neural Information Processing Systems-Volume 1.2013:926-934. [32]CHAMI I,WOLF A,JUAN D C,et al.Low-Dimensional Hy-perbolic Knowledge Graph Embeddings[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:6901-6914. [33]VU T,NGUYEN T D,NGUYEN D Q,et al.A capsule network-based embedding model for knowledge graph completion and search personalization[C]//Proceedings of the 2019 Confe-rence of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Vo-lume 1(Long and Short Papers).2019:2180-2189. [34]LIU H,WU Y,YANG Y.Analogical inference for multi-rela-tional embeddings[C]//Proceedings of the 34th International Conference on Machine Learning-Volume 70.2017:2168-2178. [35]KAZEMI S M,POOLE D.SimplE embedding for link prediction in knowledge graphs[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems.2018:4289-4300. [36]ZHANG S,TAY Y,YAO L,et al.Quaternion knowledge graph embeddings[EB/OL].(2019-10-31)[2021-12-13].https://arxiv.org/abs/1904.10281. [37]NGUYEN T D,NGUYEN D Q,PHUNG D.A Novel Embed-ding Model for Knowledge Base Completion Based on Convolutional Neural Network[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 2(Short Papers).2018:327-333. [38]XU C,LI R.Relation Embedding with Dihedral Group inKnowledge Graph[C]//Proceedings of the 57th Annual Mee-ting of the Association for Computational Linguistics.2019:263-272. [39]BALAZEVIC I,ALLEN C,HOSPEDALES T.TuckER:Tensor Factorization for Knowledge Graph Completion[C]//Procee-dings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP).2019:5188-5197. [40]BALAZEVIC I,ALLEN C,HOSPEDALES T.Multi-relational poincaré graph embeddings[J].Advances in Neural Information Processing Systems,2019,32:4463-4473. [41]DETTMERS T,MINERVINI P,STENETORP P,et al.Convolutional 2d knowledge graph embeddings[C]//AAAI Confe-rence on Artificial Intelligence.2018:1811-1818. [42]SHANG C,TANG Y,HUANG J,et al.End-to-end structure-aware convolutional networks for knowledge base completion[C]//Conference on Artificial Intelligence.2019:3060-3067. [43]BALAEVIĆ I,ALLEN C,HOSPEDALES T M.Hypernetwork knowledge graph embeddings[C]//International Conference on Artificial Neural Networks.Cham:Springer,2019:553-565. [44]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. [45]JIANG X,WANG Q,WANG B.Adaptive convolution for multi-relational learning[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long and Short Papers).2019:978-987. [46]PEZESHKPOUR P,CHEN L,SINGH S.Embedding Multimodal Relational Data for Knowledge Base Completion[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.2018:3208-3218. [47]ZHU C,CHEN M,FAN C,et al.Learning from History:Mode-ling Temporal Knowledge Graphs with Sequential Copy-Generation Networks[EB/OL].(2021-03-05)[2021-12-13].https://arxiv.org/abs/2012.08492. [48]YAO L,MAO C,LUO Y.KG-BERT:BERT for knowledgegraph completion[EB/OL].(2019-09-11)[2021-12-13].https://arxiv.org/abs/1909.03193v2. [49]NICKEL M,KIELA D.Poincaré embeddings for learning hierarchical representations[C]//Proceedings of the 31st Interna-tional Conference on Neural Information Processing Systems.2017:6341-6350. [50]SHI B,WENINGER T.ProjE:Embedding projection for know-ledge graph completion[C]//AAAI Conference on Artificial Intelligence.2017:1236-1242. [51]XUE Y,YUAN Y,XU Z,et al.Expanding holographic embeddings for knowledge completion[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems.2018:4496-4506. [52]LIN Y,LIU Z,SUN M.Knowledge representation learning with entities,attributes and relations[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.2016:2866-2872. [53]GARCIA-DURAN A,NIEPERT M.Kblrn:End-to-end learning of knowledge base representations with latent,relational,and numerical features[EB/OL].(2018-06-11)[2021-12-13].https://arxiv.org/abs/1709.04676. [54]QU M,TANG J.Probabilistic logic neural networks for reaso-ning[EB/OL].(2019-10-29)[2021-12-13].https://arxiv.org/abs/1906.08495. [55]XIAO H,HUANG M,ZHU X.TransG:A Generative Model for Knowledge Graph Embedding[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).2016:2316-2325. [56]ZHANG W,PAUDEL B,ZHANG W,et al.Interaction embeddings for prediction and explanation in knowledge graphs[C]//Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining.2019:96-104. [57]TRIVEDI R,FARAJTABAR M,WANG Y,et al.Know-Evolve:Deep Reasoning in Temporal Knowledge Graphs[EB/OL].(2017-06-21)[2021-12-13].https://arxiv.org/abs/1705.05742v1. [58]SADEGHIAN A,ARMANDPOUR M,COLAS A,et al.Chro-noR:Rotation Based Temporal Knowledge Graph Embedding[EB/OL].(2021-03-18)[2021-12-13].https://arxiv.org/abs/2103.10379. [59]JIN W,QU M,JIN X,et al.Recurrent Event Network:Auto regressive Structure Inference over Temporal Knowledge Graphs[EB/OL].(2020-10-06)[2021-12-13].https://arxiv.org/abs/1904.05530v4. [60]JENATTON R,LE ROUX N,BORDES A,et al.A latent factor model for highly multi-relational data[C]//Advances in Neural Information Processing Systems 25(NIPS 2012).2012:3176-3184. [61]LIU Q,JIANG H,EVDOKIMOV A,et al.Probabilistic reaso-ning via deep learning:Neural association models[EB/OL].(2016-08-03)[2021-12-13].https://arxiv.org/abs/1603.07704. [62]GUTMANN M U,HYVÄRINEN A.Noise-contrastive estimation of unnormalized statistical models,with applications to na-tural image statistics[J].The Journal of Machine Learning Research,2012(13):307-361. [63]ANDRIY M,YEE W T.A fast and simple algorithm for training neural probabilistic language models[EB/OL].(2012-06-27)[2021-12-13].https://arxiv.org/abs/1206.6426. [64]MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]//Conference and Workshop on Neural Information Proces-sing Systems(NIPS).2013:3111-3119. [65]GUO L,SUN Z,HU W.Learning to exploit long-term relational dependencies in knowledge graphs[C]//International Confe-rence on Machine Learning.PMLR,2019:2505-2514. [66]SUN Z,DENG Z H,NIE J Y,et al.RotatE:Knowledge Graph Embedding by Relational Rotation in Complex Space[C]//International Conference on Learning Representations.2018:1-18. [67]ZHANG Z,CAI J,ZHANG Y,et al.Learning hierarchy-aware knowledge graph embeddings for link prediction[C]//AAAI Conference on Artificial Intelligence.2020:3065-3072. [68]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. [69]KAZEMI S M,GOEL R,JAIN K,et al.Representation Learning for Dynamic Graphs:A Survey[J].Journal of Machine Learning Research,2020(21):1-73. [70]LIU Z Y,SUN M S,LIN Y K,et al.Knowledge Representation Learning:A Review[J].Journal of Computer Research and Development,2016,53(2):247-261. [71]WANG Q,MAO Z,WANG B,et al.Knowledge graph embedding:A survey of approaches and applications[J].IEEE Tran-sactions on Knowledge and Data Engineering,2017,29(12):2724-2743. [72]PAULHEIM H.Knowledge graph refinement:A survey of approaches and evaluation methods[J].Semantic Web,2017,8(3):489-508. [73]CAI H,ZHENG V W,CHANG K C.A comprehensive survey of graph embedding:Problems,techniques,and applications[J].IEEE Transactions on Knowledge and Data Engineering,2018,30(9):1616-1637. [74]LIN Y,HAN X,XIE R,et al.Knowledge representation lear-ning:A quantitative review[EB/OL].(2018-12-28)[2021-12-13].https://arxiv.org/abs/1812.10901. [75]GESESE G A,BISWAS R,SACK H.A Comprehensive Survey of Knowledge Graph Embeddings with Literals:Techniques and Applications[C]//Proceedings of DL4KG2019-Workshop on Deep Learning for Knowledge Graphs.2019:131. [76]CHEN X,JIA S,XIANG Y.A review:Knowledge reasoningover knowledge graph[J].Expert Systems with Applications,2020,141(5):1-21. [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 on Conference on Information and Knowledge Management.2017:1029-1038. [78]CHEN X,CHEN M,SHI W,et al.Embedding uncertain know-ledge graphs[C]//AAAI Conference on Artificial Intelligence.2019:3363-3370. [79]ALI M,BERRENDORF M,HOYT C T,et al.Bringing Light Into the Dark:A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework[EB/OL].(2021-11-01)[2021-12-13].https://arxiv.org/abs/2006.13365. |
[1] | LI Shujing, HUANG Zengfeng. Mixed-curve for Link Completion of Multi-relational Heterogeneous Knowledge Graphs [J]. Computer Science, 2023, 50(4): 172-180. |
[2] | MA Tinghuai, SUN Shengjie, RONG Huan, QIAN Minfeng. Knowledge Graph-to-Text Model Based on Dynamic Memory and Two-layer Reconstruction Reinforcement [J]. Computer Science, 2023, 50(3): 12-22. |
[3] | WANG Jingbin, LAI Xiaolian, LIN Xinyu, YANG Xinyi. Context-aware Temporal Knowledge Graph Completion Based on Relation Constraints [J]. Computer Science, 2023, 50(3): 23-33. |
[4] | CHEN Fuqiang, KOU Jiamin, SU Limin, LI Ke. Multi-information Optimized Entity Alignment Model Based on Graph Neural Network [J]. Computer Science, 2023, 50(3): 34-41. |
[5] | LIU Xinwei, TAO Chuanqi. Method of Java Redundant Code Detection Based on Static Analysis and Knowledge Graph [J]. Computer Science, 2023, 50(3): 65-71. |
[6] | CHEN Shurui, LIANG Ziran, RAO Yanghui. Fine-grained Semantic Knowledge Graph Enhanced Chinese OOV Word Embedding Learning [J]. Computer Science, 2023, 50(3): 72-82. |
[7] | JIANG Chuanyu, HAN Xiangyu, YANG Wenrui, LYU Bohan, HUANG Xiaoou, XIE Xia, GU Yang. Survey of Medical Knowledge Graph Research and Application [J]. Computer Science, 2023, 50(3): 83-93. |
[8] | LI Zhifei, ZHAO Yue, ZHANG Yan. Survey of Knowledge Graph Reasoning Based on Representation Learning [J]. Computer Science, 2023, 50(3): 94-113. |
[9] | WANG Xiaofei, FAN Xueqiang, LI Zhangwei. Improving RNA Base Interactions Prediction Based on Transfer Learning and Multi-view Feature Fusion [J]. Computer Science, 2023, 50(3): 164-172. |
[10] | LIU Zejing, WU Nan, HUANG Fuqun, SONG You. Hybrid Programming Task Recommendation Model Based on Knowledge Graph and Collaborative Filtering for Online Judge [J]. Computer Science, 2023, 50(2): 106-114. |
[11] | LI Junlin, OUYANG Zhi, DU Nisuo. Scene Text Detection with Improved Region Proposal Network [J]. Computer Science, 2023, 50(2): 201-208. |
[12] | SHAN Zhongyuan, YANG Kai, ZHAO Junfeng, WANG Yasha, XU Yongxin. Ontology-Schema Mapping Based Incremental Entity Model Construction and Evolution Approach of Knowledge Graph [J]. Computer Science, 2023, 50(1): 18-24. |
[13] | RONG Huan, QIAN Minfeng, MA Tinghuai, SUN Shengjie. Novel Class Reasoning Model Towards Covered Area in Given Image Based on InformedKnowledge Graph Reasoning and Multi-agent Collaboration [J]. Computer Science, 2023, 50(1): 243-252. |
[14] | XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171. |
[15] | RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207. |
|