TableNet: An approach for determining fine-grained relations for wikipedia tables

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Fetahu, B.; Anand, A.; Koutraki, M.: TableNet: An approach for determining fine-grained relations for wikipedia tables. In: The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, S. 2736-2742. DOI:

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Sum total of downloads: 129

We focus on the problem of interlinking Wikipedia tables with fine-grained table relations: equivalent and subPartOf. Such relations allow us to harness semantically related information by accessing related tables or facts therein. Determining the type of a relation is not trivial. Relations are dependent on the schemas, the cell-values, and the semantic overlap of the cell values in tables. We propose TableNet, an approach for interlinking tables with subPartOf and equivalent relations. TableNet consists of two main steps: (i) for any source table we provide an efficient algorithm to find candidate related tables with high coverage, and (ii) a neural based approach that based on the table schemas and data, determines with high accuracy the fine-grained relation. Based on an extensive evaluation with more than 3.2M tables, we show that TableNet retains more than 88% of relevant tables pairs, and assigns table relations with an accuracy of 90%.
License of this version: CC BY 4.0 Unported
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2019
Appears in Collections:Forschungszentren

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 56 43.41%
2 image of flag of United States United States 17 13.18%
3 image of flag of No geo information available No geo information available 10 7.75%
4 image of flag of India India 8 6.20%
5 image of flag of China China 6 4.65%
6 image of flag of Taiwan Taiwan 5 3.88%
7 image of flag of United Kingdom United Kingdom 3 2.33%
8 image of flag of Netherlands Netherlands 2 1.55%
9 image of flag of Japan Japan 2 1.55%
10 image of flag of Ireland Ireland 2 1.55%
    other countries 18 13.95%

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