Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (1): 310-315.doi: 10.11896/jsjkx.230300006

• Artificial Intelligence • Previous Articles     Next Articles

Knowledge Graph Completion Algorithm Based on Generative Adversarial Network and Positiveand Unlabeled Learning

HU Binhao1,2, ZHANG Jianpeng2, CHEN Hongchang2   

  1. 1 School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450002,China
    2 National Digital Switching System Engineering & Technological R&D Center(NDSC),Institute of Information Technology,University of Information Engineering,Zhengzhou 450002,China
  • Received:2023-03-01 Revised:2023-06-13 Online:2024-01-15 Published:2024-01-12
  • About author:HU Binhao,born in 1996,postgraduate.His main research interests include graph representation,knowledge graph and natural language processing.
    ZHANG Jianpeng,born in 1988,Ph.D,assistant researcher.His main research interest is big data analysis.
  • Supported by:
    National Natural Science Foundation of China(62002384) and Song Shan Laboratory(221100210700-3).

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Abstract: With the widespread application of knowledge graphs,the majority of real-world knowledge graphs suffer from the problem of incompleteness,which hinders their practical applications.As a result,it makes knowledge graph completion become a hot topic in the field of knowledge graph.However,most existing methods focus on the design of scoring functions,with only a few studies paying attention to negative sampling strategies.In the research of knowledge graph completion algorithms which aims at improving negative sampling,the methods based on generative adversarial networks(GANs) have achieved significant progress.Nonetheless,existing studies have not addressed the false negative issue,meaning that generated negative samples may contain actual facts.To address this issue,this paper proposes a knowledge graph completion algorithm based on GAN and positive-unlabeled learning.In the proposed method,GANs are utilized to generate unlabeled samples,while positive unlabeled lear-ning is employed to alleviate the false negative problem.Extensive experiments on benchmark datasets demonstrate the effectiveness and accuracy of the proposed algorithm.

Key words: Knowledge graph completion, Generative adversarial network, Positive unlabeled learning, Negative sampling

CLC Number: 

  • TP391.1
[1]KROMPASS D,BAIER S,TRESP V.Type-Constrained Representation Learning in Knowledge Graphs[C]//The Semantic Web (ISWC 2015).Cham:Springer International Publishing,2015:640-655.
[2]NICKEL M,TRESP V,KRIEGEL H P.A three-way model for collective learning on multi-relational data[C]//Proceedings of the 28th International Conference on International Conference on Machine Learning.Madison,WI,USA:Omnipress,2011:809-816.
[3]BORDES A,USUNIER N,GARCIA-DURÁN A,et al.Translating embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems-Volume 2.Red Hook,NY,USA:Curran Associates Inc.,2013:2787-2795.
[4]TROUILLON T,WELBL J,RIEDEL S,et al.Complex Embeddings for Simple Link Prediction[C]//Proceedings of The 33rd International Conference on Machine Learning.PMLR,2016:2071-2080.
[5]DETTMERS T,MINERVINI P,STENETORP P,et al.Convolutional 2D Knowledge Graph Embeddings[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2018.
[6]SUN Z,DENG Z H,NIE J Y,et al.RotatE:Knowledge Graph Embedding by Relational Rotation in Complex Space[C]//7th International Conference on Learning Representations(ICLR 2019).New Orleans,LA,USA:OpenReview.net,2019.
[7]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 Computational Linguistics.Association for Computational Linguistics,2020:6901-6914.
[8]YAO L,MAO C,LUO Y.KG-BERT:BERT for knowledgegraph completion[J].arXiv:1909.03193,2019.
[9]NIU G,ZHANG Y,LI B,et al.Rule-Guided Compositional Representation Learning on Knowledge Graphs[J].Proceedings of the AAAI Conference on Artificial Intelligence,2020,34(3):2950-2958.
[10]SHA X,SUN Z,ZHANG J.Hierarchical attentive knowledgegraph embedding for personalized recommendation[J].Electronic Commerce Research and Applications,2021,48:101071.
[11]ZHANG Y,YAO Q,SHAO Y,et al.NSCaching:Simple and Efficient Negative Sampling for Knowledge Graph Embedding[C]//2019 IEEE 35th International Conference on Data Engineering(ICDE).2019:614-625.
[12]YANG B,YIH W,HE X,et al.Embedding entities and relations for learning and inference in knowledge bases[J].arXiv:1412.6575,2014.
[13]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 Computational Linguistics.Association for Computational Linguistics,2020:6901-6914.
[14]DONG X,GABRILOVICH E,HEITZ G,et al.Knowledgevault:a web-scale approach to probabilistic knowledge fusion[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge discovery and Data Mining.New York,NY,USA:Association for Computing Machinery,2014:601-610.
[15]GUO L,SUN Z,HU W.Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs[C]//Proceedings of the 36th International Conference on Machine Learning.PMLR,2019:2505-2514.
[16]SCHLICHTKRULL M,KIPF T N,BLOEM P,et al.Modeling Relational Data with Graph Convolutional Networks[J].arXiv:1703.06103,2018.
[17]VASHISHTH S,SANYAL S,NITIN V,et al.Composition-based Multi-Relational Graph Convolutional Networks[C]//Eighth International Conference on Learning Representations.2020.
[18]CRESWELL A,WHITE T,DUMOULIN V,et al.GenerativeAdversarial Networks:An Overview[J].IEEE Signal Proces-sing Magazine,2017,35(1):53-65.
[19]YU L,ZHANG W,WANG J,et al.SeqGAN:Sequence Generative Adversarial Nets with Policy Gradient[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2017.
[20]CAI L,WANG W Y.KBGAN:Adversarial Learning for Know-ledge Graph Embeddings[C]//Proceedings of the 2018 Confe-rence of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1.New Orleans,Louisiana:Association for Computational Linguistics,2018:1470-1480.
[21]WANG P,LI S,PAN R.Incorporating GAN for Negative Sampling in Knowledge Representation Learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2018.
[22]BEKKER J,DAVIS J.Learning from positive and unlabeleddata:a survey[J].Machine Learning,2020,109(4):719-760.
[23]KIRYO R,NIU G,DU PLESSIS M C,et al.Positive-Unlabeled Learning with Non-Negative Risk Estimator[C]//Advances in Neural Information Processing Systems:Vol.30.Curran Asso-ciates,Inc.,2017.
[24]XU D,DENIL M.Positive-Unlabeled Reward Learning[C]//Proceedings of the 2020 Conference on Robot Learning.PMLR,2021:205-219.
[25]LUO C,ZHAO P,CHEN C,et al.PULNS:Positive-Unlabeled Learning with Effective Negative Sample Selector[J].Procee-dings of the AAAI Conference on Artificial Intelligence,2021,35(10):8784-8792.
[26]DU PLESSIS M C,NIU G,SUGIYAMA M.Analysis of Lear-ning from Positive and Unlabeled Data[C]//Advances in Neural Information Processing Systems.Curran Associates,Inc.,2014.
[27]PLESSIS M D,NIU G,SUGIYAMA M.Convex Formulationfor Learning from Positive and Unlabeled Data[C]//Procee-dings of the 32nd International Conference on Machine Lear-ning.PMLR,2015:1386-1394.
[28]HSIEH Y G,NIU G,SUGIYAMA M.Classification from Positive,Unlabeled and Biased Negative Data[C]//Proceedings of the 36th International Conference on Machine Learning.PMLR,2019:2820-2829.
[29]RAN Z J,SUN L F,ZOU Y S,et al.Few-Shot KnowledgeGraph Completion Model Based on Relation Learning Network[J].Computer Engineering,2023,49(9):52-59.
[30]TOUTANOVA K,CHEN D,PANTEL P,et al.RepresentingText for Joint Embedding of Text and Knowledge Bases[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.Lisbon,Portugal:Association for Computational Linguistics,2015:1499-1509.
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