research-article

Trav-SHACL: Efficiently Validating Networks of SHACL Constraints

Authors:

Mónica Figuera,

Philipp D. Rohde,

Maria-Esther VidalAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 3337 - 3348

https://doi.org/10.1145/3442381.3449877

Published: 03 June 2021 Publication History

Abstract

Knowledge graphs have emerged as expressive data structures for Web data. Knowledge graph potential and the demand for ecosystems to facilitate their creation, curation, and understanding, is testified in diverse domains, e.g., biomedicine. The Shapes Constraint Language (SHACL) is the W3C recommendation language for integrity constraints over RDF knowledge graphs. Enabling quality assements of knowledge graphs, SHACL is rapidly gaining attention in real-world scenarios. SHACL models integrity constraints as a network of shapes, where a shape contains the constraints to be fullfiled by the same entities. The validation of a SHACL shape schema can face the issue of tractability during validation. To facilitate full adoption, efficient computational methods are required. We present Trav-SHACL, a SHACL engine capable of planning the traversal and execution of a shape schema in a way that invalid entities are detected early and needless validations are minimized. Trav-SHACL reorders the shapes in a shape schema for efficient validation and rewrites target and constraint queries for fast detection of invalid entities. Trav-SHACL is empirically evaluated on 27 testbeds executed against knowledge graphs of up to 34M triples. Our experimental results suggest that Trav-SHACL exhibits high performance gradually and reduces validation time by a factor of up to 28.93 compared to the state of the art.

References

[1]

Maribel Acosta, Maria-Esther Vidal, and York Sure-Vetter. 2017. Diefficiency Metrics: Measuring the Continuous Efficiency of Query Processing Approaches. In International Semantic Web Conference ISWC. Springer, 3–19.

[2]

Medina Andreşel, Julien Corman, Magdalena Ortiz, Juan L. Reutter, Ognjen Savković, and Mantas Šimkus. 2020. Stable Model Semantics for Recursive SHACL. In ACM–The Web Conference. 1570–1580.

[3]

Sören Auer, Viktor Kovtun, Manuel Prinz, Anna Kasprzik, Markus Stocker, and Maria-Esther Vidal. 2018. Towards a Knowledge Graph for Science. In International Conference on Web Intelligence, Mining and Semantics, WIMS.

Digital Library

[4]

Sebastian R. Bader, Jaroslav Pullmann, Christian Mader, Sebastian Tramp, Christoph Quix, Andreas W. Müller, Haydar Akyürek, Matthias Böckmann, Benedikt T. Imbusch, Johannes Lipp, Sandra Geisler, and Christoph Lange. 2020. The International Data Spaces Information Model - An Ontology for Sovereign Exchange of Digital Content. In International Semantic Web Conference ISWC.

[5]

Iovka Boneva, Jose E. Labra Gayo, and Eric G. Prud’hommeaux. 2017. Semantics and Validation of Shapes Schemas for RDF. In International Semantic Web Conference ISWC. Springer, 104–120.

[6]

Stefano Ceri, Georg Gottlob, and Letizia Tanca. 1989. What you Always Wanted to Know About Datalog (And Never Dared to Ask). IEEE Trans. Knowl. Data Eng. 1, 1 (1989), 146–166.

Digital Library

[7]

Andrea Cimmino, Alba Fernández-Izquierdo, and Raúl García-Castro. 2020. Astrea: Automatic Generation of SHACL Shapes from Ontologies. In The Semantic Web – ESWC 2020. Springer, 497–513.

Digital Library

[8]

Julien Corman, Fernando Florenzano, Juan L. Reutter, and Ognjen Savković. 2019. SHACL2SPARQL: Validating a SPARQL Endpoint against Recursive SHACL Constraints. In International Semantic Web Conference ISWC Satellite Events.

[9]

Julien Corman, Fernando Florenzano, Juan L. Reutter, and Ognjen Savković. 2019. Validating SHACL Constraints over a SPARQL Endpoint. In International Semantic Web Conference ISWC. Springer, 145–163.

Digital Library

[10]

Julien Corman, Juan L. Reutter, and Ognjen Savković. 2018. Semantics and Validation of Recursive SHACL. In International Semantic Web Conference ISWC. Springer, 318–336.

[11]

Nicholson DN and Greene CS. 2020. Constructing knowledge graphs and their biomedical applications.Comput Struct Biotechnol J.(2020).

[12]

Yuanbo Guo, Zhengxiang Pan, and Jeff Heflin. 2005. LUBM: A Benchmark for OWL Knowledge Base Systems. Web Semantics 3, 2–3 (2005), 158–182.

Digital Library

[13]

Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan F. Sequeda, Steffen Staab, and Antoine Zimmermann. 2020. Knowledge Graphs. CoRR abs/2003.02320(2020).

[14]

Holger Knublauch, James A. Hendler, and Kingsley Idehen. 2011. SPIN - Overview and Motivation. W3C Submission. https://www.w3.org/Submission/2011/SUBM-spin-overview-20110222/

[15]

Holger Knublauch and Dimitris Kontokostas. 2017. Shapes Constraint Language (SHACL). W3C Recommendation. https://www.w3.org/TR/2017/REC-shacl-20170720/

[16]

Martin Leinberger, Philipp Seifer, Tjitze Rienstra, Ralf Lämmel, and Steffen Staab. 2020. Deciding SHACL Shape Containment through Description Logics Reasoning. In International Semantic Web Conference ISWC. Springer.

Digital Library

[17]

Boris Motik, Ian Horrocks, and Ulrike Sattler. 2007. Adding Integrity Constraints to OWL. In OWLED 2007 - OWL: Experiences and Directions, Vol. 258. CEUR Workshop Proceedings (CEUR-WS.org), Aachen.

[18]

Boris Motik, Ian Horrocks, and Ulrike Sattler. 2009. Bridging the Gap between OWL and Relational Databases. Web Semantics: Science, Services and Agents on the World Wide Web 7, 2(2009), 74–89.

Digital Library

[19]

Natalya Fridman Noy, Yuqing Gao, Anshu Jain, Anant Narayanan, Alan Patterson, and Jamie Taylor. 2019. Industry-scale knowledge graphs: lessons and challenges. Commun. ACM 62, 8 (2019), 36–43.

Digital Library

[20]

Paolo Pareti, George Konstantinidis, Fabio Mogavero, and Timothy J. Norman. 2020. SHACL Satisfiability and Containment. In International Semantic Web Conference ISWC. Springer.

[21]

Jorge Pérez, Marcelo Arenas, and Claudio Gutiérrez. 2009. Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34, 3 (2009), 16:1–16:45.

Digital Library

[22]

Blerina Spahiu, Andrea Maurino, and Matteo Palmonari. 2018. Towards Improving the Quality of Knowledge Graphs with Data-driven Ontology Patterns and SHACL. In International Semantic Web Conference ISWC Satellite Events. 103–117.

[23]

Blerina Spahiu, Riccardo Porrini, Matteo Palmonari, Anisa Rula, and Andrea Maurino. 2016. ABSTAT: Ontology-Driven Linked Data Summaries with Pattern Minimalization. In The Semantic Web– ESWC 2016 Satellite Events. Springer.

[24]

Jiao Tao, Evren Sirin, Jie Bao, and Deborah L. McGuinness. 2010. Integrity Constraints in OWL. In AAAI Conference on Artificial Intelligence (AAAI).

[25]

Katherine Thornton, Harold Solbrig, Gregory S. Stupp, Jose Emilio Labra Gayo, Daniel Mietchen, Eric Prud’hommeaux, and Andra Waagmeester. 2019. Using Shape Expressions (ShEx) to Share RDF Data Models and to Guide Curation with Rigorous Validation. In The Semantic Web – ESWC 2019. Springer, 606–620.

Digital Library

Cited By

Ke J(2025)Enabling Efficient and Semantic-Aware Constraint Validation in Knowledge GraphsThe Semantic Web: ESWC 2024 Satellite Events10.1007/978-3-031-78955-7_11(104-114)Online publication date: 28-Jan-2025
https://doi.org/10.1007/978-3-031-78955-7_11
Chudasama YPurohit DRohde PGercke JVidal M(2024)InterpretME: A tool for interpretations of machine learning models over knowledge graphsSemantic Web10.3233/SW-233511(1-21)Online publication date: 5-Jan-2024
https://doi.org/10.3233/SW-233511
Diaz-Honrubia ARohde PNiazmand EMenasalvas EVidal M(2024)PALADINInformation Fusion10.1016/j.inffus.2024.102557112:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102557
Show More Cited By

Recommendations

Stable Model Semantics for Recursive SHACL
WWW '20: Proceedings of The Web Conference 2020

SHACL (SHape Constraint Language) is a W3C recommendation for validating graph-based data against a set of constraints (called shapes). Importantly, SHACL allows to define recursive shapes, i.e. a shape may refer to itself, directly of indirectly. The ...
Validating Shacl Constraints over a Sparql Endpoint
The Semantic Web – ISWC 2019
Abstract
shacl (Shapes Constraint Language) is a specification for describing and validating RDF graphs that has recently become a W3C recommendation. While the language is gaining traction in the industry, algorithms for shacl constraint validation are ...
Semantics and Validation of Recursive SHACL
The Semantic Web – ISWC 2018
Abstract
With the popularity of RDF as an independent data model came the need for specifying constraints on RDF graphs, and for mechanisms to detect violations of such constraints. One of the most promising schema languages for RDF is SHACL, a recent W3C ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
Wikimedia Foundation

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 June 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WWW '21

Sponsor:

SIGWEB

WWW '21: The Web Conference 2021

April 19 - 23, 2021

Ljubljana, Slovenia

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
391
Total Downloads

Downloads (Last 12 months)97
Downloads (Last 6 weeks)5

Reflects downloads up to 31 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ke J(2025)Enabling Efficient and Semantic-Aware Constraint Validation in Knowledge GraphsThe Semantic Web: ESWC 2024 Satellite Events10.1007/978-3-031-78955-7_11(104-114)Online publication date: 28-Jan-2025
https://doi.org/10.1007/978-3-031-78955-7_11
Chudasama YPurohit DRohde PGercke JVidal M(2024)InterpretME: A tool for interpretations of machine learning models over knowledge graphsSemantic Web10.3233/SW-233511(1-21)Online publication date: 5-Jan-2024
https://doi.org/10.3233/SW-233511
Diaz-Honrubia ARohde PNiazmand EMenasalvas EVidal M(2024)PALADINInformation Fusion10.1016/j.inffus.2024.102557112:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102557
Jakubowski Mden Bussche J(2024)Compiling SHACL Into SQLThe Semantic Web – ISWC 202410.1007/978-3-031-77850-6_4(59-77)Online publication date: 27-Nov-2024
https://doi.org/10.1007/978-3-031-77850-6_4
Özçep ÖLeemhuis MWinkler MCourant RSattel TMaas J(2024)Modeling and Accessing Smart Materials with Integrity Constraints in the Shapes Constraint Language and Ontologies—The SmaDi WayAdvanced Engineering Materials10.1002/adem.202401017Online publication date: 30-Nov-2024
https://doi.org/10.1002/adem.202401017
Di Pierro DFerilli SRedavid D(2023)LPG-Based Knowledge Graphs: A Survey, a Proposal and Current TrendsInformation10.3390/info1403015414:3(154)Online publication date: 1-Mar-2023
https://doi.org/10.3390/info14030154
Aisopos FJozashoori SNiazmand EPurohit DRivas ASakor AIglesias EVogiatzis DMenasalvas ERodriguez Gonzalez AVigueras GGomez-Bravo DTorrente MHernández López RProvencio Pulla MDalianis ATriantafillou APaliouras GVidal M(2023)Knowledge graphs for enhancing transparency in health data ecosystems1Semantic Web10.3233/SW-22329414:5(943-976)Online publication date: 8-May-2023
https://doi.org/10.3233/SW-223294
DeBellis MNeches R(2023)Knowledge Representation and The Semantic Web: An Historical Overview of Influences on Emerging ToolsRecent Advances in Computer Science and Communications10.2174/266625581566622052714561016:6Online publication date: Jul-2023
https://doi.org/10.2174/2666255815666220527145610
Rabbani KLissandrini MHose K(2023)Extraction of Validating Shapes from Very Large Knowledge GraphsProceedings of the VLDB Endowment10.14778/3579075.357907816:5(1023-1032)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.14778/3579075.3579078
Yang XZhang HLi JLi S(2023)A Method for Data Quality Validation Based on Shapes Constraint Language2023 2nd International Conference on Big Data, Information and Computer Network (BDICN)10.1109/BDICN58493.2023.00024(83-87)Online publication date: Jan-2023
https://doi.org/10.1109/BDICN58493.2023.00024
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten