Learning Relational Fractals for Deep Knowledge Graph Embedding in Online Social Networks

  • Ji ZhangEmail author
  • Leonard Tan
  • Xiaohui Tao
  • Dianwei Wang
  • Josh Jia-Ching Ying
  • Xin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)


Knowledge Graphs (KGs) have deep and impactful applications in a wide-array of information networks such as natural language processing, recommendation systems, predictive analysis, recognition, classification, etc. Embedding real-life relational representations in KGs is an essential process of abstracting facts for many important data mining tasks like information retrieval, privacy and control, enrichment and so on. In this paper, we investigate the embedding of the relational fractals which are learned from the Relational Turbulence profiles in the transactions of Online Social Networks (OSNs) into KGs. These relational fractals have the capability of building both compositional-depth hierarchies and shallow-wide continuous vector spaces for more efficient computations on devices with limited resources. The results from our RFT model show accurate predictions of relational turbulence patterns in OSNs which can be used to evolve facts in KGs for more accurate and timely information representations.


Relational turbulence Deep learning Knowledge graph embedding Online Social Networks Fact evolution 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ji Zhang
    • 1
    • 2
    Email author
  • Leonard Tan
    • 1
  • Xiaohui Tao
    • 1
  • Dianwei Wang
    • 3
  • Josh Jia-Ching Ying
    • 4
  • Xin Wang
    • 5
  1. 1.University of Southern QueenslandToowoombaAustralia
  2. 2.Zhejiang LabZhejiangChina
  3. 3.Xi’an University of Posts and TelecommunicationsXi’anChina
  4. 4.National Chung Hsing UniversityTaichungTaiwan ROC
  5. 5.Southwest Jiaotong UniversityChengduChina

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