research-article

Stitching web tables for improving matching quality

Authors:

Oliver Lehmberg,

Christian BizerAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 10, Issue 11

Pages 1502 - 1513

https://doi.org/10.14778/3137628.3137657

Published: 01 August 2017 Publication History

Abstract

HTML tables on web pages ("web tables") cover a wide variety of topics. Data from web tables can thus be useful for tasks such as knowledge base completion or ad hoc table extension. Before table data can be used for these tasks, the tables must be matched to the respective knowledge base or base table. The challenges of web table matching are the high heterogeneity and the small size of the tables.

Though it is known that the majority of web tables are very small, the gold standards that are used to compare web table matching systems mostly consist of larger tables. In this experimental paper, we evaluate T2K Match, a web table to knowledge base matching system, and COMA, a standard schema matching tool, using a sample of web tables that is more realistic than the gold standards that were previously used. We find that both systems fail to produce correct results for many of the very small tables in the sample. As a remedy, we propose to stitch (combine) the tables from each web site into larger ones and match these enlarged tables to the knowledge base or base table afterwards. For this stitching process, we evaluate different schema matching methods in combination with holistic correspondence refinement. Limiting the stitching procedure to web tables from the same web site decreases the heterogeneity and allows us to stitch tables with very high precision. Our experiments show that applying table stitching before running the actual matching method improves the matching results by 0.38 in F1-measure for T2K Match and by 0.14 for COMA. Also, stitching the tables allows us to reduce the amount of tables in our corpus from 5 million original web tables to as few as 100,000 stitched tables.

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Cited By

Qiu JSong AJin JChen JZhang XFang XZhang T(2024)Matching Tabular Data to Knowledge Graph with Effective Core Column Set Discovery.ACM Transactions on the Web10.1145/369497918:4(1-27)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3694979
Wu TLi LGao HQi GWang YLi Y(2024)NPEL: Neural Paired Entity Linking in Web TablesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3652511Online publication date: 19-Mar-2024
https://dl.acm.org/doi/10.1145/3652511
Zecchini LBleifuß TSimonini GBergamaschi SNaumann F(2024)Determining the Largest Overlap between TablesProceedings of the ACM on Management of Data10.1145/36393032:1(1-26)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639303
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Stitching web tables for improving matching quality
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Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 10, Issue 11

August 2017

432 pages

ISSN:2150-8097

Editors:
Peter Boncz
CWI
,
Ken Salem
University of Waterloo

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VLDB Endowment

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Published: 01 August 2017

Published in PVLDB Volume 10, Issue 11

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Cited By

Qiu JSong AJin JChen JZhang XFang XZhang T(2024)Matching Tabular Data to Knowledge Graph with Effective Core Column Set Discovery.ACM Transactions on the Web10.1145/369497918:4(1-27)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3694979
Wu TLi LGao HQi GWang YLi Y(2024)NPEL: Neural Paired Entity Linking in Web TablesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3652511Online publication date: 19-Mar-2024
https://dl.acm.org/doi/10.1145/3652511
Zecchini LBleifuß TSimonini GBergamaschi SNaumann F(2024)Determining the Largest Overlap between TablesProceedings of the ACM on Management of Data10.1145/36393032:1(1-26)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639303
Bleifuß TPapenbrock TBläsius TSchirneck MNaumann F(2024)Discovering Functional Dependencies through Hitting Set EnumerationProceedings of the ACM on Management of Data10.1145/36392982:1(1-24)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639298
Fan GShraga RMiller R(2024)Gen-T: Table Reclamation in Data Lakes2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00272(3532-3545)Online publication date: 13-May-2024
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Fan GWang JLi YZhang DMiller R(2023)Semantics-Aware Dataset Discovery from Data Lakes with Contextualized Column-Based Representation LearningProceedings of the VLDB Endowment10.14778/3587136.358714616:7(1726-1739)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.14778/3587136.3587146
Khatiwada AFan GShraga RChen ZGatterbauer WMiller RRiedewald M(2023)SANTOS: Relationship-based Semantic Table Union SearchProceedings of the ACM on Management of Data10.1145/35886891:1(1-25)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588689
Fan GWang JLi YMiller RDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Table Discovery in Data Lakes: State-of-the-art and Future DirectionsCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589409(69-75)Online publication date: 4-Jun-2023
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Hai RKoutras CQuix CJarke M(2023)Data Lakes: A Survey of Functions and SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327010135:12(12571-12590)Online publication date: 25-Apr-2023
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Yu ZXiong RDong FWu Z(2023)High-efficient Joinable Tables Discovery in Data Lakes: A Grey Relational Model-based Approach2023 Eleventh International Conference on Advanced Cloud and Big Data (CBD)10.1109/CBD63341.2023.00047(223-228)Online publication date: 18-Dec-2023
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