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Improving Curated Web-Data Quality with Structured Harvesting and Assessment

Improving Curated Web-Data Quality with Structured Harvesting and Assessment

Kevin Chekov Feeney, Declan O'Sullivan, Wei Tai, Rob Brennan
Copyright: © 2014 |Volume: 10 |Issue: 2 |Pages: 28
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781466657014|DOI: 10.4018/ijswis.2014040103
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MLA

Feeney, Kevin Chekov, et al. "Improving Curated Web-Data Quality with Structured Harvesting and Assessment." IJSWIS vol.10, no.2 2014: pp.35-62. http://doi.org/10.4018/ijswis.2014040103

APA

Feeney, K. C., O'Sullivan, D., Tai, W., & Brennan, R. (2014). Improving Curated Web-Data Quality with Structured Harvesting and Assessment. International Journal on Semantic Web and Information Systems (IJSWIS), 10(2), 35-62. http://doi.org/10.4018/ijswis.2014040103

Chicago

Feeney, Kevin Chekov, et al. "Improving Curated Web-Data Quality with Structured Harvesting and Assessment," International Journal on Semantic Web and Information Systems (IJSWIS) 10, no.2: 35-62. http://doi.org/10.4018/ijswis.2014040103

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Abstract

This paper describes a semi-automated process, framework and tools for harvesting, assessing, improving and maintaining high-quality linked-data. The framework, known as DaCura1, provides dataset curators, who may not be knowledge engineers, with tools to collect and curate evolving linked data datasets that maintain quality over time. The framework encompasses a novel process, workflow and architecture. A working implementation has been produced and applied firstly to the publication of an existing social-sciences dataset, then to the harvesting and curation of a related dataset from an unstructured data-source. The framework's performance is evaluated using data quality measures that have been developed to measure existing published datasets. An analysis of the framework against these dimensions demonstrates that it addresses a broad range of real-world data quality concerns. Experimental results quantify the impact of the DaCura process and tools on data quality through an assessment framework and methodology which combines automated and human data quality controls.

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