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Author Meusel, Robert ♦ Ritze, Dominique ♦ Paulheim, Heiko
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Microdata ♦ Data cleaning ♦ Data integration ♦ Schema.org
Abstract Being promoted by major search engines such as Google, Yahoo!, Bing, and Yandex, Microdata embedded in web pages, especially using schema.org, has become one of the most important markup languages for the Web. However, deployed Microdata is very often not free from errors, which makes it difficult to estimate the data volume and create an accurate data profile. In addition, as the usage of global identifiers is not common, the real number of entities described by this format in the Web is hard to assess. In this article, we discuss how the subsequent application of data cleaning steps, such as duplicate detection and correction of common schema-based errors, leads to a more realistic view on the data, step by step. The cleaning steps applied include both heuristics for fixing errors as well as means to perform duplicate detection and duplicate elimination. Using the Web Data Commons Microdata corpus, we show that applying such quality improvement methods can essentially change the statistical profile of the dataset and lead to different estimates of both the number of entities as well as the class distribution within the data.
Description Author Affiliation: Research Group Data and Web Science, University of Mannheim, Mannheim, Germany (Meusel, Robert; Ritze, Dominique; Paulheim, Heiko)
ISSN 19361955
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-10-01
Publisher Place New York
e-ISSN 19361963
Journal Journal of Data and Information Quality (JDIQ)
Volume Number 8
Issue Number 1
Page Count 31
Starting Page 1
Ending Page 31


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Source: ACM Digital Library