On Integrating Knowledge Graph Embedding into SPARQL Query Processing

Hyunjoong Kang; Sanghyun Hong; Kookjin Lee; Noseong Park; Soonhyun Kwon

doi:10.1109/ICWS.2018.00064

On Integrating Knowledge Graph Embedding into SPARQL Query Processing

Hyunjoong Kang, Sanghyun Hong, Kookjin Lee, Noseong Park, Soonhyun Kwon

Graduate School

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

2 Citations (Scopus)

Abstract

SPARQL is a standard query language for knowledge graphs (KGs). However, it is hard to find correct answer if KGs are incomplete or incorrect. Knowledge graph embedding (KGE) enables answering queries on such KGs by inferring unknown knowledge and removing incorrect knowledge. Hence, our long-term goal in this line of research is to propose a new framework that integrates KGE and SPARQL, which opens various research problems to be addressed. In this paper, we solve one of the most critical problems, that is, optimizing the performance of nearest neighbor (NN) search. In our evaluations, we demonstrate that the search time of state-of-the-art NN search algorithms is improved by 40% without sacrificing answer accuracy.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	371-374
Number of pages	4
ISBN (Print)	9781538672471
DOIs	https://doi.org/10.1109/ICWS.2018.00064
Publication status	Published - 2018 Sept 5
Event	25th IEEE International Conference on Web Services, ICWS 2018 - San Francisco, United States Duration: 2018 Jul 2 → 2018 Jul 7

Publication series

Name	Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services

Conference

Conference	25th IEEE International Conference on Web Services, ICWS 2018
Country/Territory	United States
City	San Francisco
Period	18/7/2 → 18/7/7

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management

Access to Document

10.1109/ICWS.2018.00064

Cite this

Kang, H., Hong, S., Lee, K., Park, N., & Kwon, S. (2018). On Integrating Knowledge Graph Embedding into SPARQL Query Processing. In Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services (pp. 371-374). Article 8456381 (Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICWS.2018.00064

Kang, Hyunjoong ; Hong, Sanghyun ; Lee, Kookjin et al. / On Integrating Knowledge Graph Embedding into SPARQL Query Processing. Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 371-374 (Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services).

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title = "On Integrating Knowledge Graph Embedding into SPARQL Query Processing",

abstract = "SPARQL is a standard query language for knowledge graphs (KGs). However, it is hard to find correct answer if KGs are incomplete or incorrect. Knowledge graph embedding (KGE) enables answering queries on such KGs by inferring unknown knowledge and removing incorrect knowledge. Hence, our long-term goal in this line of research is to propose a new framework that integrates KGE and SPARQL, which opens various research problems to be addressed. In this paper, we solve one of the most critical problems, that is, optimizing the performance of nearest neighbor (NN) search. In our evaluations, we demonstrate that the search time of state-of-the-art NN search algorithms is improved by 40% without sacrificing answer accuracy.",

author = "Hyunjoong Kang and Sanghyun Hong and Kookjin Lee and Noseong Park and Soonhyun Kwon",

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Kang, H, Hong, S, Lee, K, Park, N & Kwon, S 2018, On Integrating Knowledge Graph Embedding into SPARQL Query Processing. in Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services., 8456381, Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services, Institute of Electrical and Electronics Engineers Inc., pp. 371-374, 25th IEEE International Conference on Web Services, ICWS 2018, San Francisco, United States, 18/7/2. https://doi.org/10.1109/ICWS.2018.00064

On Integrating Knowledge Graph Embedding into SPARQL Query Processing. / Kang, Hyunjoong; Hong, Sanghyun; Lee, Kookjin et al.
Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services. Institute of Electrical and Electronics Engineers Inc., 2018. p. 371-374 8456381 (Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services).

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

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AU - Kang, Hyunjoong

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AU - Park, Noseong

AU - Kwon, Soonhyun

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N2 - SPARQL is a standard query language for knowledge graphs (KGs). However, it is hard to find correct answer if KGs are incomplete or incorrect. Knowledge graph embedding (KGE) enables answering queries on such KGs by inferring unknown knowledge and removing incorrect knowledge. Hence, our long-term goal in this line of research is to propose a new framework that integrates KGE and SPARQL, which opens various research problems to be addressed. In this paper, we solve one of the most critical problems, that is, optimizing the performance of nearest neighbor (NN) search. In our evaluations, we demonstrate that the search time of state-of-the-art NN search algorithms is improved by 40% without sacrificing answer accuracy.

AB - SPARQL is a standard query language for knowledge graphs (KGs). However, it is hard to find correct answer if KGs are incomplete or incorrect. Knowledge graph embedding (KGE) enables answering queries on such KGs by inferring unknown knowledge and removing incorrect knowledge. Hence, our long-term goal in this line of research is to propose a new framework that integrates KGE and SPARQL, which opens various research problems to be addressed. In this paper, we solve one of the most critical problems, that is, optimizing the performance of nearest neighbor (NN) search. In our evaluations, we demonstrate that the search time of state-of-the-art NN search algorithms is improved by 40% without sacrificing answer accuracy.

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Kang H, Hong S, Lee K, Park N, Kwon S. On Integrating Knowledge Graph Embedding into SPARQL Query Processing. In Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services. Institute of Electrical and Electronics Engineers Inc. 2018. p. 371-374. 8456381. (Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services). doi: 10.1109/ICWS.2018.00064

On Integrating Knowledge Graph Embedding into SPARQL Query Processing

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