PAGE: Answering graph pattern queries via knowledge graph embedding

Sanghyun Hong; Noseong Park; Tanmoy Chakraborty; Hyunjoong Kang; Soonhyun Kwon

doi:10.1007/978-3-319-94301-5_7

PAGE: Answering graph pattern queries via knowledge graph embedding

Sanghyun Hong, Noseong Park, Tanmoy Chakraborty, Hyunjoong Kang, Soonhyun Kwon

Graduate School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

Abstract

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 PAGE that answers graph pattern queries via knowledge graph embedding methods. 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) 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, PAGE demonstrated the performance increase by up to 28% compared to baseline KGE methods.

Original language	English
Title of host publication	Big Data – BigData 2018 - 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Proceedings
Editors	Latifur Khan, Liang-Jie Zhang, Kisung Lee, Francis Y. Chin, C. L. Chen
Publisher	Springer Verlag
Pages	87-99
Number of pages	13
ISBN (Print)	9783319943008
DOIs	https://doi.org/10.1007/978-3-319-94301-5_7
Publication status	Published - 2018
Event	7th International Congress on Big Data, BigData 2018 Held as Part of the Services Conference Federation, SCF 2018 - Seattle, United States Duration: 2018 Jun 25 → 2018 Jun 30

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10968 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Congress on Big Data, BigData 2018 Held as Part of the Services Conference Federation, SCF 2018
Country/Territory	United States
City	Seattle
Period	18/6/25 → 18/6/30

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-94301-5_7

Cite this

Hong, S., Park, N., Chakraborty, T., Kang, H., & Kwon, S. (2018). PAGE: Answering graph pattern queries via knowledge graph embedding. In L. Khan, L-J. Zhang, K. Lee, F. Y. Chin, & C. L. Chen (Eds.), Big Data – BigData 2018 - 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Proceedings (pp. 87-99). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10968 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-94301-5_7

Hong, Sanghyun ; Park, Noseong ; Chakraborty, Tanmoy et al. / PAGE : Answering graph pattern queries via knowledge graph embedding. Big Data – BigData 2018 - 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Proceedings. editor / Latifur Khan ; Liang-Jie Zhang ; Kisung Lee ; Francis Y. Chin ; C. L. Chen. Springer Verlag, 2018. pp. 87-99 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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 PAGE that answers graph pattern queries via knowledge graph embedding methods. 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) 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, PAGE demonstrated the performance increase by up to 28% compared to baseline KGE methods.",

author = "Sanghyun Hong and Noseong Park and Tanmoy Chakraborty and Hyunjoong Kang and Soonhyun Kwon",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 7th International Congress on Big Data, BigData 2018 Held as Part of the Services Conference Federation, SCF 2018 ; Conference date: 25-06-2018 Through 30-06-2018",

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doi = "10.1007/978-3-319-94301-5_7",

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Hong, S, Park, N, Chakraborty, T, Kang, H & Kwon, S 2018, PAGE: Answering graph pattern queries via knowledge graph embedding. in L Khan, L-J Zhang, K Lee, FY Chin & CL Chen (eds), Big Data – BigData 2018 - 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10968 LNCS, Springer Verlag, pp. 87-99, 7th International Congress on Big Data, BigData 2018 Held as Part of the Services Conference Federation, SCF 2018, Seattle, United States, 18/6/25. https://doi.org/10.1007/978-3-319-94301-5_7

PAGE : Answering graph pattern queries via knowledge graph embedding. / Hong, Sanghyun; Park, Noseong; Chakraborty, Tanmoy et al.

Big Data – BigData 2018 - 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Proceedings. ed. / Latifur Khan; Liang-Jie Zhang; Kisung Lee; Francis Y. Chin; C. L. Chen. Springer Verlag, 2018. p. 87-99 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10968 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - PAGE

T2 - 7th International Congress on Big Data, BigData 2018 Held as Part of the Services Conference Federation, SCF 2018

AU - Hong, Sanghyun

AU - Park, Noseong

AU - Chakraborty, Tanmoy

AU - Kang, Hyunjoong

AU - Kwon, Soonhyun

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - 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 PAGE that answers graph pattern queries via knowledge graph embedding methods. 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) 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, PAGE demonstrated the performance increase by up to 28% compared to baseline KGE methods.

AB - 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 PAGE that answers graph pattern queries via knowledge graph embedding methods. 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) 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, PAGE demonstrated the performance increase by up to 28% compared to baseline KGE methods.

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DO - 10.1007/978-3-319-94301-5_7

M3 - Conference contribution

AN - SCOPUS:85049374238

SN - 9783319943008

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 87

EP - 99

BT - Big Data – BigData 2018 - 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Proceedings

A2 - Khan, Latifur

A2 - Zhang, Liang-Jie

A2 - Lee, Kisung

A2 - Chin, Francis Y.

A2 - Chen, C. L.

PB - Springer Verlag

Y2 - 25 June 2018 through 30 June 2018

ER -

Hong S, Park N, Chakraborty T, Kang H, Kwon S. PAGE: Answering graph pattern queries via knowledge graph embedding. In Khan L, Zhang L-J, Lee K, Chin FY, Chen CL, editors, Big Data – BigData 2018 - 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Proceedings. Springer Verlag. 2018. p. 87-99. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-94301-5_7

PAGE: Answering graph pattern queries via knowledge graph embedding

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