research-article

Sieve: linked data quality assessment and fusion

Authors:

Pablo N. Mendes,

Hannes Mühleisen, and

Christian BizerAuthors Info & Claims

EDBT-ICDT '12: Proceedings of the 2012 Joint EDBT/ICDT Workshops

March 2012

Pages 116 - 123

https://doi.org/10.1145/2320765.2320803

Published: 30 March 2012 Publication History

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Abstract

The Web of Linked Data grows rapidly and already contains data originating from hundreds of data sources. The quality of data from those sources is very diverse, as values may be out of date, incomplete or incorrect. Moreover, data sources may provide conflicting values for a single real-world object.

In order for Linked Data applications to consume data from this global data space in an integrated fashion, a number of challenges have to be overcome. One of these challenges is to rate and to integrate data based on their quality. However, quality is a very subjective matter, and finding a canonic judgement that is suitable for each and every task is not feasible.

To simplify the task of consuming high-quality data, we present Sieve, a framework for flexibly expressing quality assessment methods as well as fusion methods. Sieve is integrated into the Linked Data Integration Framework (LDIF), which handles Data Access, Schema Mapping and Identity Resolution, all crucial preliminaries for quality assessment and fusion.

We demonstrate Sieve in a data integration scenario importing data from the English and Portuguese versions of DBpedia, and discuss how we increase completeness, conciseness and consistency through the use of our framework.

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Zhong HYang DShi SWei LWang Y(2024)From data to insights: the application and challenges of knowledge graphs in intelligent auditJournal of Cloud Computing10.1186/s13677-024-00674-013:1Online publication date: 29-May-2024
https://doi.org/10.1186/s13677-024-00674-0
Barba-González CCaballero IVarela-Vaca ÁCruz-Lemus JGómez-López MNavas-Delgado I(2024)BIGOWL4DQ: Ontology-driven approach for Big Data quality meta-modelling, selection and reasoningInformation and Software Technology10.1016/j.infsof.2023.107378167(107378)Online publication date: Mar-2024
https://doi.org/10.1016/j.infsof.2023.107378
Jie LFeng ZZhang MJing FGuo Q(2023)Review of Knowledge Graph and Its Vertical Applications in Industry2023 42nd Chinese Control Conference (CCC)10.23919/CCC58697.2023.10240572(5151-5157)Online publication date: 24-Jul-2023
https://doi.org/10.23919/CCC58697.2023.10240572
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Index Terms

Sieve: linked data quality assessment and fusion
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Heterogeneous (hybrid) systems
2. Information systems
  1. Data management systems
  2. Information systems applications

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Published In

EDBT-ICDT '12: Proceedings of the 2012 Joint EDBT/ICDT Workshops

March 2012

265 pages

ISBN:9781450311434

DOI:10.1145/2320765

Editors:
Divesh Srivastava
AT&T Labs-Research
,
Ismail Ari
Ozyegin University, Turkey

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 March 2012

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Research-article

Funding Sources

Seventh Framework Programme

Conference

ICDT '12

ICDT '12: 15th International Conference on Database Theory

March 30, 2012

Berlin, Germany

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Overall Acceptance Rate 7 of 10 submissions, 70%

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Cited By

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https://doi.org/10.1186/s13677-024-00674-0
Barba-González CCaballero IVarela-Vaca ÁCruz-Lemus JGómez-López MNavas-Delgado I(2024)BIGOWL4DQ: Ontology-driven approach for Big Data quality meta-modelling, selection and reasoningInformation and Software Technology10.1016/j.infsof.2023.107378167(107378)Online publication date: Mar-2024
https://doi.org/10.1016/j.infsof.2023.107378
Jie LFeng ZZhang MJing FGuo Q(2023)Review of Knowledge Graph and Its Vertical Applications in Industry2023 42nd Chinese Control Conference (CCC)10.23919/CCC58697.2023.10240572(5151-5157)Online publication date: 24-Jul-2023
https://doi.org/10.23919/CCC58697.2023.10240572
Zhang SBenis NCornet R(2023)Automated approach for quality assessment of RDF resourcesBMC Medical Informatics and Decision Making10.1186/s12911-023-02182-823:S1Online publication date: 10-May-2023
https://doi.org/10.1186/s12911-023-02182-8
Andersen JCazalens SLamarre PMaillot P(2023)A Framework to Assess Knowledge Graphs Accountability2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00034(213-220)Online publication date: 26-Oct-2023
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Sahi LLaborde RKandi MSibilla MMacilotti GAbdelmalek BFerreira A(2023)Towards Reliable Collaborative Data Processing Ecosystems: Survey on Data Quality Criteria2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom60117.2023.00345(2456-2464)Online publication date: 1-Nov-2023
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Ahmed ASherif MNgomo A(2023)NELLIE: Never-Ending Linking for Linked Open DataIEEE Access10.1109/ACCESS.2023.330069411(84957-84973)Online publication date: 2023
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Iglesias EJozashoori SVidal M(2023)Scaling up knowledge graph creation to large and heterogeneous data sourcesJournal of Web Semantics10.1016/j.websem.2022.10075575(100755)Online publication date: Jan-2023
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Li HShi ZPan CZhao DSun N(2023)Cybersecurity knowledge graphs construction and quality assessmentComplex & Intelligent Systems10.1007/s40747-023-01205-110:1(1201-1217)Online publication date: 24-Aug-2023
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Alzetta CTorre IKoceva F(2023)Annotation Protocol for Textbook Enrichment with Prerequisite Knowledge GraphTechnology, Knowledge and Learning10.1007/s10758-023-09682-629:1(197-228)Online publication date: 21-Sep-2023
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