PAGE: Answering Graph Pattern Queries via Knowledge Graph Embedding

  • Sanghyun Hong
  • Noseong Park
  • Tanmoy Chakraborty
  • Hyunjoong Kang
  • Soonhyun Kwon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10968)


Answering graph pattern queries have been highly dependent on a technique—i.e., subgraph matching, however, this approach is ineffective when knowledge graphs include incorrect or incomplete information. In this paper, we present a method called \(\mathtt {PAGE}\) that answers graph pattern queries via knowledge graph embedding methods. \(\mathtt {PAGE}\) computes the energy (or uncertainty) of candidate answers with the learned embeddings and chooses the lower-energy candidates as answers. Our method has the two advantages: (1) \(\mathtt {PAGE}\) is able to find latent answers hard to be found via subgraph matching and (2) presents a robust metric that enables us to compute the plausibility of an answer. In evaluations with two popular knowledge graphs, Freebase and NELL, \(\mathtt {PAGE}\) demonstrated the performance increase by up to 28% compared to baseline KGE methods.


Graph databases Graph query answering Knowledge graph embedding 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sanghyun Hong
    • 1
  • Noseong Park
    • 2
  • Tanmoy Chakraborty
    • 3
  • Hyunjoong Kang
    • 4
  • Soonhyun Kwon
    • 4
  1. 1.University of MarylandCollege ParkUSA
  2. 2.University of North CarolinaCharlotteUSA
  3. 3.Indraprastha Institute of Information Technology DelhiDelhiIndia
  4. 4.Electronics and Telecommunications Research InstituteDaejeonSouth Korea

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