计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 204-211.doi: 10.11896/jsjkx.210400129
王杰, 李晓楠, 李冠宇
WANG Jie, LI Xiao-nan, LI Guan-yu
摘要: 现有的知识图谱补全模型通常将多源信息整合为实体和关系学习单一的静态特征表示,但无法表征不同上下文中出现的实体和关系的细差含义和动态属性,即实体和关系在涉及不同的三元组时可能有着不同的角色和含义,并因此表现出不同的属性。为此,提出了一种自适应注意力网络用于知识图谱补全,引入自适应注意力建模每个特征维度对特定任务的贡献程度,为目标实体和关系生成动态可变的嵌入表示。具体而言,所提模型通过定义邻居编码器和路径聚合器来处理实体邻域子图中的两种结构,自适应地调整邻居实体和关系路径的注意力得分,以捕获逻辑上与任务最相关的属性特征,为实体和关系赋予符合当前任务的细粒度语义。在链接预测任务中的实验结果表明,所提模型在FB15K-237数据集中的MeanRank指标比PathCon降低了6.9%,Hits@1比PathCon提高了2.3%;在稀疏数据集NELL-995和DDB14上,其Hits@1分别达到了87.9%和98%,证明了引入自适应注意力机制能够有效提取实体和关系的动态属性,为二者生成更全面的表示形式,从而提高知识图谱补全精度。
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[1]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. [2]MILLER G A.WordNet:A Lexical Database for English[J].Communications of the ACM,1995,38(11):39-41. [3]XIANG R,WU Z,HE W,et al.Cotype:Joint Extraction ofTyped Entities and Relations with Knowledge Bases[C]//Proceedings of the 26th International Conference on World Wide Web.Perth:ACM,2017:1015-1024. [4]HUANG X,ZHANG J,LI D,et al.Knowledge Graph Embedding based Question Answering [C]//Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining.Melbourne:ACM,2019:105-113. [5]WANG H,ZHAO M,XIE X,et al.Knowledge Graph Convolutional Networks for Recommender Systems[C]//Proceedings of the World Wide Web Conference.San Francisco:ACM,2019:3307-3313. [6]DING J H,JIA W J.Summary of knowledge graph completion algorithms[J].Information and Communication Technology,2018,12(1):56-62. [7]ARORA S.A survey on graph neural networks for knowledgegraph completion[J].arXiv:2007.12374,2020. [8]OH B,SEO S,LEE K H.Knowledge graph completion by context-aware convolutional learning with multi-hop neighborhoods[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.2018:257-266. [9]WANG C,SHA Y.Neighborhood Aggregation Embedding Modelfor Link Prediction in Knowledge Graphs[C]//International Conference on Web Engineering.Cham:Springer,2020:188-203. [10]BORDES A,USUNIER N,et al.Translating Embeddings forModeling Multi-relational Data[C]//Proceedings of the NIPS.Cambridge:MIT Press,2013:2787-2795. [11]WANG Z,ZHANG J, FENG J, et al.Knowledge Graph Embedding by Translating on Hyperplanes[C]//Proceedings of the AAAI.Menlo Park,CA:AAAI, 2014:1112-1119. [12]LIN Y,LIU Z,SUN M,et al.Learning Entity and Relation Embeddings For Knowledge Graph Completion[C]//Proceedings of the AAAI.Menlo Park,CA:AAAI,2014:2181-2187. [13]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 North American Chapter of the Asso-ciation for Computational Linguistics.San Diego California:The Association for Computational Linguistics,2016:460-466. [14]SUN Z,DENG Z,NIE J,et al.Rotate:Knowledge Graph Embedding by Relational Rotation in Complex Space[J].arXiv:1902.10197,2019. [15]YANG B,YIH W,HE X,et al.Embedding Entities and Relations for Learning and Inference in Knowledge Bases[J].arXiv:1412.6575,2014. [16]TROUILLON T,WELBL J,RIEDEL S,et al.Complex Embeddings for Simple Link Prediction[C]//Proceedings of the 33nd International Conference on Machine Learning.New York City:JMLR.org,2016:2071-2080. [17]KAZEMI S,POOLE D.Simple Embedding for Link Prediction in Knowledge Graphs[C]//Advances in Neural Information Processing Systems.Canada,2018:4284-4295. [18]ZHANG S,YI T,YAO L,et al.Quaternion Knowledge Graph Embeddings[C]//Advances in Neural Information Processing Systems.Vancouver,2019:2731-2741. [19]DETTMERS T,MINERVINI P,STENETORP P,et al.Convolutional 2D Knowledge Graph Embeddings[C]//Proceedings of the AAAI.New Orleans:AAAIPress,2018:1811-1818. [20]DAI Q N,TU D N,NGUYEN D Q,et al.A Novel Embedding Model for Knowledge Base Completion based on Convolutional Neural Network[C]//Proceedings of the NAACL.New Or-leans:Association for Computational Linguistics,2018:327-333. [21]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 NAACL-HLT.Minneapolis:Association for ComputationalLinguistics,2019:2180-2189. [22]SADEGHIAN A,ARMANDPOUR M,DING P,et al.DRUM:End-To-End Differentiable Rule Mining On Knowledge Graphs [C]//Advances in Neural Information Processing Systems.Vancouver,2019:15321-15331. [23]LIN Y,LIU Z,LUAN H,et al.Modeling Relation Paths for Representation Learning of Knowledge Bases[C]//Proceedings of the EMNLP.Lisbon,The Association for Computational Linguistics,2015:705-714. [24]NIU G,LI YANG,TANG C,et al.Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Know-ledge Graph Completion[J].arXiv:2104.13095,2021. [25]WANG K,LIU Y,XU X,et al.Enhancing knowledge graph embedding by composite neighbors for link prediction[J].Computing,2020,102(12):2587-2606. [26]LIU X,TAN H,CHEN Q,et al.RAGAT:Relation AwareGraph Attention Network for Knowledge Graph Completion[J].IEEE Access,2021,9:20840-20849. [27]CAI L,YAN B,MAI G,et al.TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction[C]//Proceedings of the 10th International Confe-rence on Knowledge Capture.Marina Del Rey:ACM,2019:131-138. [28]SCHLICHTKRULL M,KIPF T N,BLOEM P,et al.Modeling Relational Data with Graph Convolutional Networks[C]//15th International Conference on Extended Semantic Web Confe-rence.ESWC,2018:593-607. [29]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.Brussels:Association for Computational Linguistics,2018:1980-1990. [30]WANG H,REN H,LESKOVEC J.Entity Context and Relational Paths for Knowledge Graph Completion[J].arXiv:2002.06757,2020. [31]ZHANG C,YAO H,HUANG C,et al.Few-Shot KnowledgeGraph Completion [C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York:AAAI Press,2020:3041-3048. [32]ZHAO X J,JIA Y,LI A P,et al.Research on Link Prediction Model Based on Hierarchical Attention Mechanism[J].Journal of Communications,2021,42(3):36-44. [33]XIE Z,ZHOU G,LIU J,et al.ReInceptionE:Relation-aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding[C]//Proceedings of the 58th ACL.Online:Association for Computational Linguistics,2020:5929-5939. [34]XIONG W,HOANG T,WANG W Y.DeepPath:A Reinforcement Learning Method for Knowledge Graph Reasoning[C]//Proceedings of the EMNLP.Copenhagen:Association for Computational Linguistics,2017:564-573. [35]ZHANG Z,ZHUANG F,ZHU H,et al.Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion[J].Proceedings of the AAAI Conference on Artificial Intelligence,2020,34(5):9612-9619. [36]SUN Z,VASHISHTH S,SANYAL S,et al.A Re-evaluation of Knowledge Graph Completion Methods[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Online:Association for Computational Linguistics,2020:5516-5522. |
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