Inferring Missing Categorical Information in Noisy and Sparse Web Markup

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Tempelmeier, N.; Demidova, E.; Dietze, S.: Inferring Missing Categorical Information in Noisy and Sparse Web Markup. In: Proceedings of the 2018 World Wide Web Conference (WWW '18), S. 1297-1306. DOI: https://doi.org/10.1145/3178876.3186028

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/4771

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Sum total of downloads: 119




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Abstract: 
Embedded markup of Web pages has seen widespread adoption throughout the past years driven by standards such as RDFa and Microdata and initiatives such as schema.org, where recent studies show an adoption by 39% of all Web pages already in 2016. While this constitutes an important information source for tasks such as Web search, Web page classification or knowledge graph augmentation, individual markup nodes are usually sparsely described and often lack essential information. For instance, from 26 million nodes describing events within the Common Crawl in 2016, 59% of nodes provide less than six statements and only 257,000 nodes (0.96%) are typed with more specific event subtypes. Nevertheless, given the scale and diversity of Web markup data, nodes that provide missing information can be obtained from the Web in large quantities, in particular for categorical properties. Such data constitutes potential training data for inferring missing information to significantly augment sparsely described nodes. In this work, we introduce a supervised approach for inferring missing categorical properties in Web markup. Our experiments, conducted on properties of events and movies, show a performance of 79% and 83% F1 score correspondingly, significantly outperforming existing baselines.
License of this version: CC BY 4.0 Unported
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2018
Appears in Collections:Forschungszentren

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 74 62.18%
2 image of flag of United States United States 21 17.65%
3 image of flag of China China 6 5.04%
4 image of flag of No geo information available No geo information available 3 2.52%
5 image of flag of Netherlands Netherlands 2 1.68%
6 image of flag of Israel Israel 2 1.68%
7 image of flag of France France 2 1.68%
8 image of flag of Taiwan Taiwan 1 0.84%
9 image of flag of Singapore Singapore 1 0.84%
10 image of flag of Italy Italy 1 0.84%
    other countries 6 5.04%

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