survey

Large-scale Semantic Integration of Linked Data: A Survey

Authors:

Michalis Mountantonakis,

Yannis TzitzikasAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 5

Article No.: 103, Pages 1 - 40

https://doi.org/10.1145/3345551

Published: 13 September 2019 Publication History

Abstract

A large number of published datasets (or sources) that follow Linked Data principles is currently available and this number grows rapidly. However, the major target of Linked Data, i.e., linking and integration, is not easy to achieve. In general, information integration is difficult, because (a) datasets are produced, kept, or managed by different organizations using different models, schemas, or formats, (b) the same real-world entities or relationships are referred with different URIs or names and in different natural languages,<?brk?>(c) datasets usually contain complementary information, (d) datasets can contain data that are erroneous, out-of-date, or conflicting, (e) datasets even about the same domain may follow different conceptualizations of the domain, (f) everything can change (e.g., schemas, data) as time passes. This article surveys the work that has been done in the area of Linked Data integration, it identifies the main actors and use cases, it analyzes and factorizes the integration process according to various dimensions, and it discusses the methods that are used in each step. Emphasis is given on methods that can be used for integrating several datasets. Based on this analysis, the article concludes with directions that are worth further research.

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Index Terms

Large-scale Semantic Integration of Linked Data: A Survey
1. Information systems
  1. Data management systems
    1. 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 Computing Surveys

ACM Computing Surveys Volume 52, Issue 5

September 2020

791 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3362097

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

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Publication History

Published: 13 September 2019

Accepted: 01 July 2019

Revised: 01 May 2019

Received: 01 July 2018

Published in CSUR Volume 52, Issue 5

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