Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 126-131.doi: 10.11896/jsjkx.200300115

• Artificial Intelligence • Previous Articles     Next Articles

Cross-media Knowledge Graph Construction for Electric Power Metering Based on Semantic Correlation

XIAO Yong, QIAN Bin, ZHOU Mi   

  1. Electric Power Research Institute,CSG,Guangzhou 510663,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:XIAO Yong,born in 1979,Ph.D,professorate senior engineer.His main research interests include electric power metering technology and so on.
    ZHOU Mi,born in 1992,master,engineer.Her main research interests include electric power metering technology and so on.

Abstract: Facing the field of electric power metering,this paper proposes a cross-media knowledge graph construction method based on semantic correlation.There is a semantic gap between the low-level features of different types of media,which is difficult to directly associate.But different types of media describing the same entity have the same semantic tag information at the high-level semantics.That is the so-called semantic association.Based on the characteristics of knowledge in the field of electric power metering,this paper completes the cross-media knowledge graph through core steps such as semantic analysis and feature extraction,semantic association mining,and cross-media ontology construction.Experiment results show that the proposed method is effective and can support cross-media retrieval applications in the field of electric power metering.

Key words: Cross-media, Electric power metering, Knowledge graph, Semantic association

CLC Number: 

  • TP317
[1] YANG Y.Cross-media Information Technology and Application[M].Publishing House of Electronics Industry,2014.
[2] HUANG Y,ZHANG H.Cross-media Retrieval Algorithm Basedon Latent Semantic Topic Enhancement[J].Journal of Computer Applications,2017,37(4):1061-1064,1110.
[3] SHANG X,ZHANG H,CHUA T S.Deep Learning GenericFeatures for Cross-Media Retrieval[C]//International Conference on.Springer-Verlag New York,Inc.2016.
[4] PENG Y X,ZHU W W,ZHAO Y,et al.Cross-media analysis and reasoning:advances and directions[J].Frontiers of Information Technology & Electronic Engineering,2017,18(1):44-57.
[5] XIONG H X,YANG Z R,JIANG W X.Research on Semantic Relevance of Multimodal Data in Cross-media Knowledge Graph Construction[J].Information studies:Theory & Application,2019,42(2):17-22,28.
[6] WEI Y C.Semantic Classification and Retrieval of Cross-media Data[D].Beijing:Beijing Jiaotong University,2016.
[7] FAN M,WANG W,DONG P,et al.Cross-media Retrieval byLearning Rich Semantic Embeddings of Multimedia[C]//Acm on Multimedia Conference.ACM,2017.
[8] WANG Y Z.Electric Power Metering Technology[M].ChinaElectric Power Press,2015.
[9] XIAO Y,ZHAO W,LUO R X,et al.Survey of Digital Electric Power Metering Algorithms[J].Electrical Measurement & Ins-trumentation,2018,55(7):1-7.
[10] LIU Q,LI Y,DUAN H,et al.Overview of Knowledge GraphConstruction Technology[J].Journal of Computer Research and Development,2016(3):582-600.
[11] LU T,JIN Y,SU F,et al.Content-oriented multimedia document understanding through cross-media correlation[J].Multimedia Tools and Applications,2015,74(18):8105-8135.
[12] PENG X.Research on Cross-media Semantic Retrieval of Digital Library Based on Deep Learning[J].Information Research,2018(2):16-19.
[13] LOWE D G.Distinctive Image Features from Scale-InvariantKeypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[14] FOOTE J T.Content-based retrieval of music and audio[C]//Multimedia Storage & Archiving Systems II.International Society for Optics and Photonics,1997.
[15] HOFMANNT.Unsupervised Learning by Probabilistic LatentSemantic Analysis[J].Machine Learning,2001,42(1/2):177-196.
[16] HUANG X W,YAN M,SANG J T,et al.Knowledge Association and Collaborative Application Across Networks Based on Association Rule Mining[J].Computer Science,2016,43(7):51-56.
[17] MING J R,HE C.Research on Cross-media Retrieval Method of Digital Library Based on Semantic Association Mining[J].Lib-rary and Information Service,2013(7):101-105.
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