Towards empty answers in SPARQL: Approximating querying with RDF embedding

Meng Wang; Ruijie Wang; Jun Liu; Yihe Chen; Lei Zhang; Guilin Qi

doi:10.1007/978-3-030-00671-6_30

Towards empty answers in SPARQL: Approximating querying with RDF embedding

Meng Wang
, Ruijie Wang
, Jun Liu
, Yihe Chen
, Lei Zhang
, Guilin Qi

Faculty of Electronic and Information Engineering

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

46 Scopus citations

Abstract

The LOD cloud offers a plethora of RDF data sources where users discover items of interest by issuing SPARQL queries. A common query problem for users is to face with empty answers: given a SPARQL query that returns nothing, how to refine the query to obtain a non-empty set? In this paper, we propose an RDF graph embedding based framework to solve the SPARQL empty-answer problem in terms of a continuous vector space. We first project the RDF graph into a continuous vector space by an entity context preserving translational embedding model which is specially designed for SPARQL queries. Then, given a SPARQL query that returns an empty set, we partition it into several parts and compute approximate answers by leveraging RDF embeddings and the translation mechanism. We also generate alternative queries for returned answers, which helps users recognize their expectations and refine the original query finally. To validate the effectiveness and efficiency of our framework, we conduct extensive experiments on the real-world RDF dataset. The results show that our framework can significantly improve the quality of approximate answers and speed up the generation of alternative queries.

Original language	English
Title of host publication	The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings
Editors	Mari Carmen Suárez-Figueroa, Valentina Presutti, Lucie-Aimee Kaffee, Elena Simperl, Marta Sabou, Denny Vrandecic, Irene Celino, Kalina Bontcheva
Publisher	Springer Verlag
Pages	513-529
Number of pages	17
ISBN (Print)	9783030006709
DOIs	https://doi.org/10.1007/978-3-030-00671-6_30
State	Published - 2018
Event	17th International Semantic Web Conference, ISWC 2018 - Monterey, United States Duration: 8 Oct 2018 → 12 Oct 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11136 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Semantic Web Conference, ISWC 2018
Country/Territory	United States
City	Monterey
Period	8/10/18 → 12/10/18

Keywords

Empty-answer
Graph embedding
RDF
SPARQL

Access to Document

10.1007/978-3-030-00671-6_30

Cite this

Wang, M., Wang, R., Liu, J., Chen, Y., Zhang, L., & Qi, G. (2018). Towards empty answers in SPARQL: Approximating querying with RDF embedding. In M. C. Suárez-Figueroa, V. Presutti, L.-A. Kaffee, E. Simperl, M. Sabou, D. Vrandecic, I. Celino, & K. Bontcheva (Eds.), The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings (pp. 513-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11136 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00671-6_30

Wang, Meng ; Wang, Ruijie ; Liu, Jun et al. / Towards empty answers in SPARQL : Approximating querying with RDF embedding. The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings. editor / Mari Carmen Suárez-Figueroa ; Valentina Presutti ; Lucie-Aimee Kaffee ; Elena Simperl ; Marta Sabou ; Denny Vrandecic ; Irene Celino ; Kalina Bontcheva. Springer Verlag, 2018. pp. 513-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{6945a5fe83b24a868deb3c86aeec9c8b,

title = "Towards empty answers in SPARQL: Approximating querying with RDF embedding",

abstract = "The LOD cloud offers a plethora of RDF data sources where users discover items of interest by issuing SPARQL queries. A common query problem for users is to face with empty answers: given a SPARQL query that returns nothing, how to refine the query to obtain a non-empty set? In this paper, we propose an RDF graph embedding based framework to solve the SPARQL empty-answer problem in terms of a continuous vector space. We first project the RDF graph into a continuous vector space by an entity context preserving translational embedding model which is specially designed for SPARQL queries. Then, given a SPARQL query that returns an empty set, we partition it into several parts and compute approximate answers by leveraging RDF embeddings and the translation mechanism. We also generate alternative queries for returned answers, which helps users recognize their expectations and refine the original query finally. To validate the effectiveness and efficiency of our framework, we conduct extensive experiments on the real-world RDF dataset. The results show that our framework can significantly improve the quality of approximate answers and speed up the generation of alternative queries.",

keywords = "Empty-answer, Graph embedding, RDF, SPARQL",

author = "Meng Wang and Ruijie Wang and Jun Liu and Yihe Chen and Lei Zhang and Guilin Qi",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 17th International Semantic Web Conference, ISWC 2018 ; Conference date: 08-10-2018 Through 12-10-2018",

year = "2018",

doi = "10.1007/978-3-030-00671-6\_30",

language = "英语",

isbn = "9783030006709",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "513--529",

editor = "Su{\'a}rez-Figueroa, \{Mari Carmen\} and Valentina Presutti and Lucie-Aimee Kaffee and Elena Simperl and Marta Sabou and Denny Vrandecic and Irene Celino and Kalina Bontcheva",

booktitle = "The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings",

}

Wang, M, Wang, R, Liu, J, Chen, Y, Zhang, L & Qi, G 2018, Towards empty answers in SPARQL: Approximating querying with RDF embedding. in MC Suárez-Figueroa, V Presutti, L-A Kaffee, E Simperl, M Sabou, D Vrandecic, I Celino & K Bontcheva (eds), The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11136 LNCS, Springer Verlag, pp. 513-529, 17th International Semantic Web Conference, ISWC 2018, Monterey, United States, 8/10/18. https://doi.org/10.1007/978-3-030-00671-6_30

Towards empty answers in SPARQL: Approximating querying with RDF embedding. / Wang, Meng; Wang, Ruijie; Liu, Jun et al.
The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings. ed. / Mari Carmen Suárez-Figueroa; Valentina Presutti; Lucie-Aimee Kaffee; Elena Simperl; Marta Sabou; Denny Vrandecic; Irene Celino; Kalina Bontcheva. Springer Verlag, 2018. p. 513-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11136 LNCS).

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

TY - GEN

T1 - Towards empty answers in SPARQL

T2 - 17th International Semantic Web Conference, ISWC 2018

AU - Wang, Meng

AU - Wang, Ruijie

AU - Liu, Jun

AU - Chen, Yihe

AU - Zhang, Lei

AU - Qi, Guilin

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - The LOD cloud offers a plethora of RDF data sources where users discover items of interest by issuing SPARQL queries. A common query problem for users is to face with empty answers: given a SPARQL query that returns nothing, how to refine the query to obtain a non-empty set? In this paper, we propose an RDF graph embedding based framework to solve the SPARQL empty-answer problem in terms of a continuous vector space. We first project the RDF graph into a continuous vector space by an entity context preserving translational embedding model which is specially designed for SPARQL queries. Then, given a SPARQL query that returns an empty set, we partition it into several parts and compute approximate answers by leveraging RDF embeddings and the translation mechanism. We also generate alternative queries for returned answers, which helps users recognize their expectations and refine the original query finally. To validate the effectiveness and efficiency of our framework, we conduct extensive experiments on the real-world RDF dataset. The results show that our framework can significantly improve the quality of approximate answers and speed up the generation of alternative queries.

AB - The LOD cloud offers a plethora of RDF data sources where users discover items of interest by issuing SPARQL queries. A common query problem for users is to face with empty answers: given a SPARQL query that returns nothing, how to refine the query to obtain a non-empty set? In this paper, we propose an RDF graph embedding based framework to solve the SPARQL empty-answer problem in terms of a continuous vector space. We first project the RDF graph into a continuous vector space by an entity context preserving translational embedding model which is specially designed for SPARQL queries. Then, given a SPARQL query that returns an empty set, we partition it into several parts and compute approximate answers by leveraging RDF embeddings and the translation mechanism. We also generate alternative queries for returned answers, which helps users recognize their expectations and refine the original query finally. To validate the effectiveness and efficiency of our framework, we conduct extensive experiments on the real-world RDF dataset. The results show that our framework can significantly improve the quality of approximate answers and speed up the generation of alternative queries.

KW - Empty-answer

KW - Graph embedding

KW - RDF

KW - SPARQL

UR - https://www.scopus.com/pages/publications/85054785786

U2 - 10.1007/978-3-030-00671-6_30

DO - 10.1007/978-3-030-00671-6_30

M3 - 会议稿件

AN - SCOPUS:85054785786

SN - 9783030006709

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

SP - 513

EP - 529

BT - The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings

A2 - Suárez-Figueroa, Mari Carmen

A2 - Presutti, Valentina

A2 - Kaffee, Lucie-Aimee

A2 - Simperl, Elena

A2 - Sabou, Marta

A2 - Vrandecic, Denny

A2 - Celino, Irene

A2 - Bontcheva, Kalina

PB - Springer Verlag

Y2 - 8 October 2018 through 12 October 2018

ER -

Wang M, Wang R, Liu J, Chen Y, Zhang L, Qi G. Towards empty answers in SPARQL: Approximating querying with RDF embedding. In Suárez-Figueroa MC, Presutti V, Kaffee LA, Simperl E, Sabou M, Vrandecic D, Celino I, Bontcheva K, editors, The Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings. Springer Verlag. 2018. p. 513-529. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00671-6_30

Towards empty answers in SPARQL: Approximating querying with RDF embedding

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Keywords

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