Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (4): 172-180.doi: 10.11896/jsjkx.220500135

• Artificial Intelligence • Previous Articles     Next Articles

Mixed-curve for Link Completion of Multi-relational Heterogeneous Knowledge Graphs

LI Shujing, HUANG Zengfeng   

  1. School of Data Science,Fudan University,Shanghai 200433,China
  • Received:2022-05-16 Revised:2022-09-12 Online:2023-04-15 Published:2023-04-06
  • About author:LI Shujing,born in 1984,postgraduate.Her main research interests knowledge graph representation learning,graph convolutional network.
    HUANG Zengfeng,born in 1985,Ph.D,professor,Ph.D supervisor.His main research interests include graph representation learning,differential privacy,bandits and online learning,distributed and streaming algorithms,communication complexity and lower bounds.

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Abstract: Knowledge graphs(KGs)has gradually become valuable asset in the field of AI.However,a major problem is that there are many missing edges in the existing KGs.KGs representation learning can effectively solve this problem.The quality of representation learning depends on how well the geometry of the embedding space matches the structure of the data.Euclidean space has been the main force for embeddings;hyperbolic andspherical spaces gaining popularity due to their ability to better embed new types of structured data.However,most data are highly heterogeneous,the single-space modeling leads to large information distortion.To solve this problem,inspired by MuRP model,mixed-curve space model is proposed to provide representations suitable for heterogeneous structural data.Firstly,the Descartes product of Euclidean hyperbolic and spherical spaces is used to construct mixed space.Then,a graph attention mechanism is designed to obtain the importance of relationship.Experimental results on three KGs benchmark datasets show that the proposed model can effectively alleviate the problems caused by heterostructural embedding in low-dimensional spaces with constant curvature.The proposed method is applied to the cold start problem of recommender system,and the corresponding indicators have been improved to a certain extent.

Key words: Representation learning, Heterogeneous knowledge graph, Mixed-curve space, Link prediction, Space weight

CLC Number: 

  • TP391
[1]BERANT J,CHOU A,FROSTIG R,et al.Semantic parsing on freebase from question-answer pairs[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.2013:1533-1154.
[2]BERANT J,LIANG P.Semantic parsing via paraphrasing[C]//Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics(Volume1:Long Papers).2014:1415-1425.
[3]HE H,BALAKRISHNAN A,ERIC M,et al.Learning symme-tric collaborative dialogue agents with dynamic knowledge graph embeddings[J].arXiv:1704.07130,2017.
[4]KEIZER S,GUHE M,CUAYAHUITL H,et al.Evaluatingpersuasion strategies and deep reinforcement learning methods for negotiation dialogue agents[C]//ACL.2017:480-484.
[5]WANG H,ZHANG F,WANG J,et al.Ripplenet:Propagating user preferences on the Knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Confe-rence on Information and Knowledge Management.2018:417-426.
[6]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 Knowledge Disco-very & Data Mining.2019:950-958.
[7]LIU J,DUAN L.A survey on knowledge graph-based recommender systems[C]//2021 IEEE 5th Advanced Information Technology,Electronic and Automation Control Conference(IAEAC).IEEE,2021:2450-2453.
[8]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[C]//Advances in Neural Information Processing Systems.2013:2787-2795.
[9]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(Volume 1:Long Papers).Beijing,China:Association for Computational Linguistics,2015:687-696.
[10]SUN Z,DENG Z,NIE J,et al.Rotate:Knowledge graph em-bedding by relational rotation in complex space[J/OL].CoRR,2019,abs/190-2.10197.http://arxiv.org/abs/1902.10197.
[11]SARKAR R.Low distortion delaunay embedding of trees in hyperbolic plane[C]//International Symposium on Graph Dra-wing.Berlin／Heidelberg:Springer,2011:355-366.
[12]SALA F,DE SA C,GU A,et al.Representation tradeoffs for hyperbolic embeddings[C]//International Conference on Machine Learning.PMLR,2018:4460-4469.
[13]SAXENA C,CHAUDHARY M,MENG H.Cross-lingual Word Embeddings in Hyperbolic Space[J].arXiv:2205.01907,2022.
[14]MIKOLOV T,YIH W,ZWEIG G.Linguistic regularities in continuous space word representations[C]//Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2013:746-751.
[15]WANG Z,ZHANG J,FENG J,et al.Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2014:1112-1119.
[16]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.
[17]TROUILLON T,WELBL J,RIEDEL S,et al.Complex embeddings for simple link prediction[C]//International Conference on Machine Learning.PMLR,2016:2071-2080.
[18]ZHANG Z,CAI J,ZHANG Y,et al.Learning hierarchy-aware knowledge graph embeddings for link prediction[C]//Procee-dings of the AAAI Conference on Artifical Intelligence.2020:3065-3072.
[19]BALAZEVIC I,ALLEN C,HOSPEDALES T.Multi-relational poincaré graph embeddings[J].Advances in Neural Information Processing System,2019,32:4463-4473.
[20]MONTELLA S,ROJAS-BARAHONA L,HEINECKE J.Hy-perbolic temporal knowledge graph embe-ddings with relational and time curvatures[J].arXiv:2106.04311,2021.
[21]YANG M,ZHOU M,KALANDER M,et al.Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.2021:1975-1985.
[22]GUO N,LIU X,LI S,et al.HCGR:Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation[J].arXiv:2107.05366,2021.
[23]ZHANG S,CHEN H,MING X,et al.Where are we in embedding spaces? A comprehensive analysis on network embedding approaches for recommender systems[J].arXiv:2105.08908,2021.
[24]LI Y,CHEN H,SUN X,et al.Hyperbolic hypergraphs for sequential recommenda-tion[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management.2021:988-997.
[25]YU T,DE SA C.Hyla:Hyperbolic laplacian features for graph learning[J].arXiv:2202.06854,2022.
[26]ZHANG C,GAO J.Hype-han:Hyperbolic hierarchical attention network for semanticembedding[C]//Proceedings of the Twenty-Ninth International Conference on Interna-tional Joint Conferences on Artificial Intelligence.2021:3990-3996.
[27]LIU W,WEN Y,YU Z,et al.Sphereface:Deep hypersphere embedding for face recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:212-220.
[28]WILSON C,HANCOCK E R,PEKALSA E,et al.Spherical and hyperbolic embeddings of data[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(11):2255-2269.
[29]YANG M,ZHOU M,LI Z,et al.Hyperbolic Graph Neural Networks:A Review of Methods and Applications[J].arXiv:2202.13852,2022.
[30]YANG M,ZHOU M,LIU J,et al.HRCF:Enhancing collaborative filtering via hyperbolic geometric regularization[C]//Proceedings of the ACM Web Conference 2022.2022:2462-2471.
[31]NATHANI D,CHAUHAN J,SHARMA C,et al.Lea-rning attention-based embeddings for relation prediction in knowledge graphs[J].arXiv:1906.01195,2019.
[32]GU A,SALA F,GUNEL B,et al.Learning mixed-curvaturerepresentations in product spaces[C]//International Conference on Learning Representations.2018.
[33]KOPEK O,GANEA O E,BECIGNEUL G.Mixed-curvaturevariational autoencoders[J].arXiv:1911.08411,2019.
[34]FICKEN F A.The riemannian and affine differ-ential geometry of product-spaces[J].Annals of Mathematics,1939,40(4):892-913.
[35]TURAGA P K,SRIVASTAVA A.Riemannian computing incomputer vision:volume 1[M].Springer,2016.
[36]BANSAL T,JUAN D C,RAVI S,et al.A2n:Attending toneighbors for knowledge graph inference[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.2019:4387-4392.
[37]MILLER G A.Wordnet:A lexical database for English[J].Communication of ACM,1995,38(11):39-41.
[38]BOLLACKER K,EVANS C,PARITOSH P,et al.Freebase:A collaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data.2008:1247-1250.
[39]SUCHANEK F M,KASNECIA G,WEIKUM G.Yago:A large ontology from wikipedia and wordnet[J].Journal of Web Semantics,2008,6(3):203-217.
[40]DETTMERS T,MINERVINI P,STENETOPR P,et al.Convolutional 2d knowledge graph embeddings[J/OL].CoRR,2017,abs/1707.01476.http://arxiv.org/abs/1707.01476.
[41]VASHISHTH S,SANYAL S,NITIN V,et al.Composition-based multi-relational graph convolutional networks[J].arXiv:1911.03082,2020.
[42]ZHANG J,SHI X,ZHAO S,et al.Star-gcn:Stac-ked and reconstructed graph convolutional networks for recommender systems[J].arXiv:1905.13129,2019.
[43]MA H,KING I,LYU M R.Effective missing data prediction for collaborative filtering[C]//Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.2007:39-46.
[44]ZHANG F,YUAN N J,LIAN D,et al.Collaborative know-ledge base embedding for recom-mender systems[C]//Procee-dings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2016:353-362.
[45]WANG Z,LIN G,TAN H,et al.Ckan:collaborative know-ledge-aware attentive network for recommender systems[C]//Proc of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.2020:219-228.
[46]WANG H,ZHAO M,XIE X,et al.Knowledge graph convolutional networks for recommender systems[C]//The World Wide Web Conference.2019:3307-3313.
[47]CHEN Y,YANG M,ZHANG Y,et al.Modeling scale-freegraphs with hyperbolic geometry for knowledge-aware recommendation[C]//Proceedings of the 15th ACM International Conference on Web Search and Data Mining.2022:94-102.
[48]LEI J P,OUYANG D T,ZHANG L M.Relation domain and range completion method based on knowledge graph embedding[J].Journal of Jilin University(Engineering and Technology Edition),2022,52(1):154-161.
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