research-article

Linked data processing provenance: towards transparent and reusable linked data integration

Authors:

Tuan-Dat Trinh,

Fajar J. Ekaputra,

Elmar Kiesling,

Andreas Rauber,

A Min TjoaAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 88 - 96

https://doi.org/10.1145/3106426.3106495

Published: 23 August 2017 Publication History

Abstract

The growth of Linked Data has created a promising environment for data exploration and a growing number of tools allow users to interactively integrate data from various sources. Eliciting the reliability of the results of such ad-hoc integration processes, consistently recreating those results, and identifying changes upon re-execution, however, can be difficult. Automated process provenance trail creation can provide major benefits in this context, because (i) it enables users to trace the contribution of individual sources and processing steps to the final outcome and judge whether the result can be trusted; (ii) it ensures repeatability and raises the trustworthiness of results; (iii) it ideally enables reconstruction of Linked Data integration processes from the provenance information embedded in the final result. In this paper, we present a provenance model that facilitates automatic generation of semantic provenance information for generic Linked Data integration processes. We implement the generic model in a collaborative mashup environment and evaluate it by means of an example application. We find that the model provides a solid foundation for verifiability and contributes towards making Linked Data integration processes more open, transparent, and reusable, which is crucial in domains where the origin of data is essential, such as, for instance, statistical analyses, scientific research, and data journalism.

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Cited By

Souza CJúnior JPrazeres CPereira Ada Rocha L(2021)An Ontological Model and Services for Capturing and Tracking Provenance in Decentralized Social NetworksProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3470482.3479637(221-228)Online publication date: 5-Nov-2021
https://dl.acm.org/doi/10.1145/3470482.3479637
Sikos LPhilp D(2020)Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge GraphsData Science and Engineering10.1007/s41019-020-00118-0Online publication date: 8-May-2020
https://doi.org/10.1007/s41019-020-00118-0
D. Fernández JJ. Ekaputra FRuswono Aryan PAzzam AKiesling E(2019)Privacy-aware Linked WidgetsCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3317591(508-514)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3317591
Show More Cited By

Index Terms

Linked data processing provenance: towards transparent and reusable linked data integration
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Data provenance
    2. Information integration
  2. World Wide Web
    1. Web data description languages
      1. Semantic web description languages
        Resource Description Framework (RDF)

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cover image ACM Conferences

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Copyright © 2017 ACM.

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SIGAI: ACM Special Interest Group on Artificial Intelligence
TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
Web Intelligence Consortium

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Published: 23 August 2017

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WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Souza CJúnior JPrazeres CPereira Ada Rocha L(2021)An Ontological Model and Services for Capturing and Tracking Provenance in Decentralized Social NetworksProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3470482.3479637(221-228)Online publication date: 5-Nov-2021
https://dl.acm.org/doi/10.1145/3470482.3479637
Sikos LPhilp D(2020)Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge GraphsData Science and Engineering10.1007/s41019-020-00118-0Online publication date: 8-May-2020
https://doi.org/10.1007/s41019-020-00118-0
D. Fernández JJ. Ekaputra FRuswono Aryan PAzzam AKiesling E(2019)Privacy-aware Linked WidgetsCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3317591(508-514)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3317591
dos Reis Cda Silva WMartins LPinheiro RVictorino MHolanda MChbeir RManolopoulos YIlarri SPapadopoulos A(2019)Enhancing Open Government Data With Data ProvenanceProceedings of the 11th International Conference on Management of Digital EcoSystems10.1145/3297662.3365791(142-149)Online publication date: 12-Nov-2019
https://dl.acm.org/doi/10.1145/3297662.3365791
Yu FZhou BLu TGu N(2018)Research on Data Provenance Model for Multidisciplinary CollaborationComputer Supported Cooperative Work and Social Computing10.1007/978-981-13-3044-5_3(32-49)Online publication date: 11-Dec-2018
https://doi.org/10.1007/978-981-13-3044-5_3

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