Computer Science ›› 2020, Vol. 47 ›› Issue (4): 189-193.doi: 10.11896/jsjkx.190300024

• Artificial Intelligence • Previous Articles     Next Articles

Knowledge Graph Representation Based on Improved Vector Projection Distance

LI Xin-chao, LI Pei-feng, ZHU Qiao-ming   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,ChinaProvincial Key Laboratory for Computer Information Processing Technology,Suzhou,Jiangsu 215006,China
  • Received:2019-03-08 Online:2020-04-15 Published:2020-04-15
  • Contact: LI Pei-feng,born in 1971,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include natural language processing and machine learning.
  • About author:LI Xin-chao,born in 1995,postgradua-te.His main research interests include natural language processing and representation learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61836007,61772354,61773276).

Abstract: Representation learning is of great value in knowledge graph reasoning,which realizes the computability of knowledge by embedding entities and relationships into a low-dimensional space.The representation learning model based on vector projection distance has better ability of knowledge representation on complex relationships.However,the model is easily susceptible to irrelevant information,especially when dealing with one-to-one relationships,and it still has space to improve performance in representing one-to-many,many-to-one and many-to-many relationships.In this paper,we proposed an improved representation learning model SProjE,which introduces an adaptive metric method to reduce the weight of noise information and optimizes the loss function to improve the loss weight of complex relation triples.The proposed model is suitable for large scale knowledge graph representation learning.At last,the experimental results on the WN18 and FB15k data sets show that SProjE achieves significant and consistent improvements compared with the existing models and methods.

Key words: Knowledge graph, Representation learning, Adaptive metric, Entity link prediction

CLC Number: 

  • TP391.1
[1]BORDES A,WESTON J,USUNIER N.Open Question Answering with Weakly Supervised EmbeddingModels[J/OL].
[2014-04-16].https://arxiv.org/pdf/1404.4326.pdf.
[2]ZHENG Z,SI X,LI F,et al.Entity Disambiguation with Freebase[C]//2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.IEEE Computer Society,2012.
[3]DAIBER J,JAKOB M.Improving efficiency and accuracy inmultilingual entity extraction[C]//Proceedings of the 9th International Conference on Semantic Systems.ACM,2013:121-124.
[4]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.2013:1533-1544.
[5]WANG Q.Knowledge graph embedding:A survey of approaches and applications [J].IEEE Transactions on Knowledge and Data Engineering,2017,29(12):2724-2743.
[6]SHI B,WENINGER T.ProjE:Embedding Projection forKnowledge Graph Completion[C]// Proc of AAAI 2017.San Francisco:AAAI,2017:1236-1242.
[7]MIKOLOV T,SUTSKEVER I,CHEN,et al.Distributed representations of words and phrases and their compositionality[C]//Proc of NIPS 2013.Cambridge,MA:MIT Press,2013:3111-3119.
[8]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[C]//Proc of NIPS 2013.Cambridge,MA:MIT Press,2013:2787-2795.
[9]WANG Z,ZHANG J,FENG J,et al.Knowledge graph embedding by translating on hyperplanes[C]//Proc of AAAI 2014.Menlo Park,CA:AAAI,2014:1112-1119.
[10]LIN Y K,LIU Z Y,SUN M S,et al.LearingEntity and Relation Embeddings for Knowledge Graph Completion[C]//Proc of AAAI 2015.Menlo Park,CA:AAAI,2015:2181-2187.
[11]JI G,HE S,XU L,et al.Knowledge Graph Embedding viaDynamic Mapping Matrix[C]//Proc of ACL 2015.Beijing,China:ACL,2015:687-696.
[12]JI G,LIU K,HE S,et al.Knowledge Graph Completion withAdaptive Sparse Transfer Matrix[C]// Thirtieth Aaai Confe-rence on Artificial Intelligence.AAAI Press,2016.
[13]FAN M,QIANG Z,CHANG E,et al.Transition-based knowledge graph embedding with relational mapping properties[C]//Proc. of PACLIC.2014.
[14]XIAO H,HUANG M L,HAO Y,et al.TransA:An adaptiveapproach for knowledge graph embedding [J/OL].[2015-09-28].https://arxiv.org/pdf/1509.05490.pdf.
[15]NICKEL M,TRESP V,KRIEGEL H P.A Three-Way Modelfor Collective Learning on Multi-Relational Data[C]//Proc of ICML 2011.New York,USA:ACM,2011:809-816.
[16]BORDES A,GLOROT X,WESTON J,et al.A semantic matching energy function for learning with multi-relational data[J].Machine Learning,2014,94(2):233-259.
[17]SOCHER R,CHEN D,MANNING C D,et al.Reasoning with neural tensor networks for knowledge base completion[C]//Proc of NIPS 2013.Cambridge,MA:MIT Press,2013:926-934.
[18]BOLLACKER K,EVANS C,PARITOSH P,et al.Freebase:acollaboratively created graph database for structuring human knowledge[C]// Sigmod Conference.2008.
[19]KINGMA D,BA J.Adam:A method for stochastic optimization [J/OL].[2017-01-30].https://arxiv.org/pdf/1412.6980.pdf.
[20]BORDES A,WESTON J,COLLOBERT R,et al.Learningstructured embeddings of knowledge bases[C]//Proc. of AAAI 2011.Menlo Park,CA:AAAI,2011:301-306.
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