research-article

RDF shape induction using knowledge base profiling

Authors:

Nandana Mihindukulasooriya,

Mohammad Rifat Ahmmad Rashid,

Giuseppe Rizzo,

Raúl García-Castro,

Marco TorchianoAuthors Info & Claims

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

Pages 1952 - 1959

https://doi.org/10.1145/3167132.3167341

Published: 09 April 2018 Publication History

Abstract

Knowledge Graphs (KGs) are becoming the core of most artificial intelligent and cognitive applications. Popular KGs such as DBpedia and Wikidata have chosen the RDF data model to represent their data. Despite the advantages, there are challenges in using RDF data, for example, data validation. Ontologies for specifying domain conceptualizations in RDF data are designed for entailments rather than validation. Most ontologies lack the granular information needed for validating constraints. Recent work on RDF Shapes and standardization of languages such as SHACL and ShEX provide better mechanisms for representing integrity constraints for RDF data. However, manually creating constraints for large KGs is still a tedious task. In this paper, we present a data driven approach for inducing integrity constraints for RDF data using data profiling. Those constraints can be combined into RDF Shapes and can be used to validate RDF graphs. Our method is based on machine learning techniques to automatically generate RDF shapes using profiled RDF data as features. In the experiments, the proposed approach achieved 97% precision in deriving RDF Shapes with cardinality constraints for a subset of DBpedia data.

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Cited By

Carriero VGroth PPresutti V(2024)Empirical ontology design patterns and shapes from WikidataSemantic Web10.3233/SW-243613(1-25)Online publication date: 20-Mar-2024
https://doi.org/10.3233/SW-243613
Seifer PHernández DLämmel RStaab SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT QueriesProceedings of the ACM Web Conference 202410.1145/3589334.3645550(2064-2074)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645550
Duan XChaves-Fraga DDerom ODimou A(2024)SCOOP All the Constraints’ Flavours for Your Knowledge GraphThe Semantic Web10.1007/978-3-031-60635-9_13(217-234)Online publication date: 19-May-2024
https://doi.org/10.1007/978-3-031-60635-9_13
Show More Cited By

Index Terms

RDF shape induction using knowledge base profiling
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Data management systems
    1. Information integration
      1. Data cleaning

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cover image ACM Conferences

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

April 2018

2327 pages

ISBN:9781450351911

DOI:10.1145/3167132

Conference Chairs:
Hisham M. Haddad
Kennesaw State University
,
Roger L. Wainwright
University of Tulsa
,
Richard Chbeir
University of Pau & Pays Adour, France

Copyright © 2018 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 09 April 2018

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Ministerio de Economía, Industria y Competitividad

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SAC 2018

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SIGAPP

SAC 2018: Symposium on Applied Computing

April 9 - 13, 2018

Pau, France

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Carriero VGroth PPresutti V(2024)Empirical ontology design patterns and shapes from WikidataSemantic Web10.3233/SW-243613(1-25)Online publication date: 20-Mar-2024
https://doi.org/10.3233/SW-243613
Seifer PHernández DLämmel RStaab SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT QueriesProceedings of the ACM Web Conference 202410.1145/3589334.3645550(2064-2074)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645550
Duan XChaves-Fraga DDerom ODimou A(2024)SCOOP All the Constraints’ Flavours for Your Knowledge GraphThe Semantic Web10.1007/978-3-031-60635-9_13(217-234)Online publication date: 19-May-2024
https://doi.org/10.1007/978-3-031-60635-9_13
Burse RBertolotto MMcArdle G(2023)Enhancing Semantic Web Technologies Using Lexical Auditing Techniques for Quality Assurance of Biomedical OntologiesBioMedInformatics10.3390/biomedinformatics30400593:4(962-984)Online publication date: 1-Nov-2023
https://doi.org/10.3390/biomedinformatics3040059
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: 6-Mar-2023
https://dl.acm.org/doi/10.14778/3579075.3579078
Duan XChaves-Fraga DDimou A(2023)XSD2SHACL: Capturing RDF Constraints from XML SchemaProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627565(214-222)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627565
Zhao XLi FYang H(2023) RDFS (c) Schema Inconsistency Checking Based on a Key Instance and Its Query Rewriting IEEE Access10.1109/ACCESS.2023.323408411(6122-6132)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3234084
Felin RFaron CTettamanzi A(2023)A Framework to Include and Exploit Probabilistic Information in SHACL Validation ReportsThe Semantic Web10.1007/978-3-031-33455-9_6(91-104)Online publication date: 28-May-2023
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Candela G(2023)An automatic data quality approach to assess semantic data from cultural heritage institutionsJournal of the Association for Information Science and Technology10.1002/asi.2476174:7(866-878)Online publication date: 21-Apr-2023
https://dl.acm.org/doi/10.1002/asi.24761
Ghiasnezhad Omran PTaylor KRodríguez Méndez SHaller A(2022)Learning SHACL shapes from knowledge graphsSemantic Web10.3233/SW-22306314:1(101-121)Online publication date: 30-Nov-2022
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