A framework for linked data fusion and quality assessment

Mohammad Khodizadeh-Nahari; Nasser Ghadiri; Zahra Jafarifard; Ahmad Baraani Dastjerdi; Joerg R. Sack

doi:10.1109/ICWR.2017.7959307

A framework for linked data fusion and quality assessment

Mohammad Khodizadeh-Nahari, Nasser Ghadiri, Zahra Jafarifard, Ahmad Baraani Dastjerdi, Joerg R. Sack

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

8 Citations (Scopus)

Abstract

![CDATA[The growth of semantic web technologies underpins the ever-increasing development of linked data and their applications. In recent years, the number of linked data sources has been raised from 12 to more than 2973 sets. The datasets are managed as decentralized sources, and their quality is a serious concern. The assessment of the quality of linked data is a key to adopting them in different fields because each data set has been developed by a different group, using various methods and tools. Moreover, crowdsourcing contributes as one of the main strategies in data collection. This contribution is seen in the tourism industry or E-commerce fields and deserves attention. The qualitative and quantitative diversity of such data is higher than those generated by official organizations and firms. In this paper, we first overview and evaluate the dimensions and measures for the quality assessment of data. Then, we present a novel framework as a solution for improving linked data quality evaluation and data fusion. Finally, we adopt several tools to assess the quality of data of some reputable data sources using the proposed framework.]]

Original language	English
Title of host publication	Proceedings of the 3rd International Conference on Web Research (ICWR), Tehran, Iran, 19-20 April 2017
Publisher	IEEE
Pages	67-72
Number of pages	6
ISBN (Print)	9781538604205
DOIs	https://doi.org/10.1109/ICWR.2017.7959307
Publication status	Published - 2017
Event	International Conference on Web Research - Duration: 19 Apr 2017 → …

Conference

Conference	International Conference on Web Research
Period	19/04/17 → …

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10.1109/ICWR.2017.7959307

Cite this

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title = "A framework for linked data fusion and quality assessment",

abstract = "![CDATA[The growth of semantic web technologies underpins the ever-increasing development of linked data and their applications. In recent years, the number of linked data sources has been raised from 12 to more than 2973 sets. The datasets are managed as decentralized sources, and their quality is a serious concern. The assessment of the quality of linked data is a key to adopting them in different fields because each data set has been developed by a different group, using various methods and tools. Moreover, crowdsourcing contributes as one of the main strategies in data collection. This contribution is seen in the tourism industry or E-commerce fields and deserves attention. The qualitative and quantitative diversity of such data is higher than those generated by official organizations and firms. In this paper, we first overview and evaluate the dimensions and measures for the quality assessment of data. Then, we present a novel framework as a solution for improving linked data quality evaluation and data fusion. Finally, we adopt several tools to assess the quality of data of some reputable data sources using the proposed framework.]]",

author = "Mohammad Khodizadeh-Nahari and Nasser Ghadiri and Zahra Jafarifard and {Baraani Dastjerdi}, Ahmad and Sack, {Joerg R.}",

year = "2017",

doi = "10.1109/ICWR.2017.7959307",

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pages = "67--72",

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publisher = "IEEE",

note = "International Conference on Web Research ; Conference date: 19-04-2017",

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T1 - A framework for linked data fusion and quality assessment

AU - Khodizadeh-Nahari, Mohammad

AU - Ghadiri, Nasser

AU - Jafarifard, Zahra

AU - Baraani Dastjerdi, Ahmad

AU - Sack, Joerg R.

PY - 2017

Y1 - 2017

N2 - ![CDATA[The growth of semantic web technologies underpins the ever-increasing development of linked data and their applications. In recent years, the number of linked data sources has been raised from 12 to more than 2973 sets. The datasets are managed as decentralized sources, and their quality is a serious concern. The assessment of the quality of linked data is a key to adopting them in different fields because each data set has been developed by a different group, using various methods and tools. Moreover, crowdsourcing contributes as one of the main strategies in data collection. This contribution is seen in the tourism industry or E-commerce fields and deserves attention. The qualitative and quantitative diversity of such data is higher than those generated by official organizations and firms. In this paper, we first overview and evaluate the dimensions and measures for the quality assessment of data. Then, we present a novel framework as a solution for improving linked data quality evaluation and data fusion. Finally, we adopt several tools to assess the quality of data of some reputable data sources using the proposed framework.]]

AB - ![CDATA[The growth of semantic web technologies underpins the ever-increasing development of linked data and their applications. In recent years, the number of linked data sources has been raised from 12 to more than 2973 sets. The datasets are managed as decentralized sources, and their quality is a serious concern. The assessment of the quality of linked data is a key to adopting them in different fields because each data set has been developed by a different group, using various methods and tools. Moreover, crowdsourcing contributes as one of the main strategies in data collection. This contribution is seen in the tourism industry or E-commerce fields and deserves attention. The qualitative and quantitative diversity of such data is higher than those generated by official organizations and firms. In this paper, we first overview and evaluate the dimensions and measures for the quality assessment of data. Then, we present a novel framework as a solution for improving linked data quality evaluation and data fusion. Finally, we adopt several tools to assess the quality of data of some reputable data sources using the proposed framework.]]

UR - https://hdl.handle.net/1959.7/uws:68937

U2 - 10.1109/ICWR.2017.7959307

DO - 10.1109/ICWR.2017.7959307

M3 - Conference Paper

SN - 9781538604205

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BT - Proceedings of the 3rd International Conference on Web Research (ICWR), Tehran, Iran, 19-20 April 2017

PB - IEEE

T2 - International Conference on Web Research

Y2 - 19 April 2017

ER -

A framework for linked data fusion and quality assessment

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