research-article

Knowledge exploration using tables on the web

Authors:

Fernando Chirigati,

Hao ZhangAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 10, Issue 3

Pages 193 - 204

https://doi.org/10.14778/3021924.3021935

Published: 01 November 2016 Publication History

Abstract

The increasing popularity of mobile device usage has ushered in many features in modern search engines that help users with various information needs. One of those needs is Knowledge Exploration, where related documents are returned in response to a user query, either directly through right-hand side knowledge panels or indirectly through navigable sections underneath individual search results. Existing knowledge exploration features have relied on a combination of Knowledge Bases and query logs.

In this paper, we propose Knowledge Carousels of two modalities, namely sideways and downwards, that facilitate exploration of IS-A and HAS-A relationships, respectively, with regard to an entity-seeking query, based on leveraging the large corpus of tables on the Web. This brings many technical challenges, including associating correct carousels with the search entity, selecting the best carousel from the candidates, and finding titles that best describe the carousel. We describe how we address these challenges and also experimentally demonstrate through user studies that our approach produces better result sets than baseline approaches.

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Cited By

Günther MThiele MGonsior JLehner W(2021)Pre-Trained Web Table Embeddings for Table DiscoveryProceedings of the Fourth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management10.1145/3464509.3464892(24-31)Online publication date: 20-Jun-2021
https://dl.acm.org/doi/10.1145/3464509.3464892
Lees ABarbosa LKorn FSouza Silva LWu YYu C(2021)Collocating News Articles with Structured Web Tables✱Companion Proceedings of the Web Conference 202110.1145/3442442.3452326(393-401)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442442.3452326
Zhang SBalog K(2021)Semantic Table Retrieval Using Keyword and Table QueriesACM Transactions on the Web10.1145/344169015:3(1-33)Online publication date: 13-May-2021
https://dl.acm.org/doi/10.1145/3441690
Show More Cited By

Knowledge exploration using tables on the web
1. Information systems

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Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 10, Issue 3

November 2016

216 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 November 2016

Published in PVLDB Volume 10, Issue 3

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Cited By

Günther MThiele MGonsior JLehner W(2021)Pre-Trained Web Table Embeddings for Table DiscoveryProceedings of the Fourth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management10.1145/3464509.3464892(24-31)Online publication date: 20-Jun-2021
https://dl.acm.org/doi/10.1145/3464509.3464892
Lees ABarbosa LKorn FSouza Silva LWu YYu C(2021)Collocating News Articles with Structured Web Tables✱Companion Proceedings of the Web Conference 202110.1145/3442442.3452326(393-401)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442442.3452326
Zhang SBalog K(2021)Semantic Table Retrieval Using Keyword and Table QueriesACM Transactions on the Web10.1145/344169015:3(1-33)Online publication date: 13-May-2021
https://dl.acm.org/doi/10.1145/3441690
Zhang SBalog K(2020)Web Table Extraction, Retrieval, and AugmentationACM Transactions on Intelligent Systems and Technology10.1145/337211711:2(1-35)Online publication date: 25-Jan-2020
https://dl.acm.org/doi/10.1145/3372117
Zhang SMeij EBalog KReinanda R(2020)Novel Entity Discovery from Web TablesProceedings of The Web Conference 202010.1145/3366423.3380205(1298-1308)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380205
Hancock BLee HYu C(2019)Generating Titles for Web TablesThe World Wide Web Conference10.1145/3308558.3313399(638-647)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313399
Zhang QZhang HSellam TWu EBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Mining Precision Interfaces From Query LogsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3319872(988-1005)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3319872
Korn FWang XWu YYu CBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Automatically Generating Interesting Facts from Wikipedia TablesProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3314043(349-361)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3314043
Huang SLiu JKorn FWang XWu YMarkowitz DYu CTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Contextual Fact Ranking and Its Applications in Table Synthesis and CompressionProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330980(285-293)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330980
Chapman ASimperl EKoesten LKonstantinidis GIbáñez LKacprzak EGroth P(2019)Dataset search: a surveyThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00564-x29:1(251-272)Online publication date: 24-Aug-2019
https://dl.acm.org/doi/10.1007/s00778-019-00564-x
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