research-article

On-the-fly Table Generation

Authors:

Krisztian BalogAuthors Info & Claims

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Pages 595 - 604

https://doi.org/10.1145/3209978.3209988

Published: 27 June 2018 Publication History

Abstract

Many information needs revolve around entities, which would be better answered by summarizing results in a tabular format, rather than presenting them as a ranked list. Unlike previous work, which is limited to retrieving existing tables, we aim to answer queries by automatically compiling a table in response to a query. We introduce and address the task of on-the-fly table generation: given a query, generate a relational table that contains relevant entities (as rows) along with their key properties (as columns). This problem is decomposed into three specific subtasks: (i) core column entity ranking, (ii) schema determination, and (iii) value lookup. We employ a feature-based approach for entity ranking and schema determination, combining deep semantic features with task-specific signals. We further show that these two subtasks are not independent of each other and can assist each other in an iterative manner. For value lookup, we combine information from existing tables and a knowledge base. Using two sets of entity-oriented queries, we evaluate our approach both on the component level and on the end-to-end table generation task.

References

[1]

Ahmad Ahmadov, Maik Thiele, Julian Eberius, Wolfgang Lehner, and Robert Wrembel . 2015. Towards a Hybrid Imputation Approach Using Web Tables. Proc. of BDC '15. 21--30.

[2]

Chandra Sekhar Bhagavatula, Thanapon Noraset, and Doug Downey . 2013. Methods for Exploring and Mining Tables on Wikipedia Proc. of IDEA '13. 18--26.

Digital Library

[3]

Chandra Sekhar Bhagavatula, Thanapon Noraset, and Doug Downey . 2015. TabEL: Entity Linking in Web Tables. In Proc. of ISWC 2015. 425--441.

Digital Library

[4]

Michael J. Cafarella, Alon Halevy, and Nodira Khoussainova . 2009. Data Integration for the Relational Web. Proc. of VLDB Endow. Vol. 2 (2009), 1090--1101.

Digital Library

[5]

Michael J. Cafarella, Alon Halevy, Daisy Zhe Wang, Eugene Wu, and Yang Zhang . 2008. WebTables: Exploring the Power of Tables on the Web. Proc. of VLDB Endow. Vol. 1 (2008), 538--549.

Digital Library

[6]

Eric Crestan and Patrick Pantel . 2011. Web-scale Table Census and Classification. In Proc. of WSDM '11. 545--554.

Digital Library

[7]

Anish Das Sarma, Lujun Fang, Nitin Gupta, Alon Halevy, Hongrae Lee, Fei Wu, Reynold Xin, and Cong Yu . 2012. Finding Related Tables. In Proc. of SIGMOD '12. 817--828.

Digital Library

[8]

Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang . 2014. Knowledge Vault: A Web-scale Approach to Probabilistic Knowledge Fusion Proc. of KDD '14. 601--610.

Digital Library

[9]

Yixing Fan, Liang Pang, JianPeng Hou, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng . 2017. MatchZoo: A Toolkit for Deep Text Matching. arXiv preprint arXiv:1707.07270 (2017).

[10]

J.L. Fleiss and others . 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin Vol. 76 (1971), 378--382.

[11]

Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft . 2016. A Deep Relevance Matching Model for Ad-hoc Retrieval Proc. of CIKM '16. 55--64.

Digital Library

[12]

Faegheh Hasibi, Fedor Nikolaev, Chenyan Xiong, Krisztian Balog, Svein Erik Bratsberg, Alexander Kotov, and Jamie Callan . 2017. DBpedia-Entity V2: A Test Collection for Entity Search Proc. of SIGIR '17. 1265--1268.

Digital Library

[13]

Diederik P. Kingma and Jimmy Ba . 2014. Adam: A Method for Stochastic Optimization. http://arxiv.org/abs/1412.6980

[14]

Arlind Kopliku, Mohand Boughanem, and Karen Pinel-Sauvagnat . 2011. Towards a Framework for Attribute Retrieval. In Proc. of CIKM '11. 515--524.

Digital Library

[15]

Oliver Lehmberg, Dominique Ritze, Robert Meusel, and Christian Bizer . 2016. A Large Public Corpus of Web Tables Containing Time and Context Metadata Proc. of WWW '16 Companion. 75--76.

Digital Library

[16]

Oliver Lehmberg, Dominique Ritze, Petar Ristoski, Robert Meusel, Heiko Paulheim, and Christian Bizer . 2015. The Mannheim Search Join Engine. Web Semant. Vol. 35 (2015), 159--166.

Digital Library

[17]

Girija Limaye, Sunita Sarawagi, and Soumen Chakrabarti . 2010. Annotating and Searching Web Tables Using Entities, Types and Relationships. Proc. of VLDB Endow. Vol. 3 (2010), 1338--1347.

Digital Library

[18]

Emir Munoz, Aidan Hogan, and Alessandra Mileo . 2014. Using Linked Data to Mine RDF from Wikipedia's Tables Proc. of WSDM '14. 533--542.

Digital Library

[19]

Thanh Tam Nguyen, Quoc Viet Hung Nguyen, Weidlich Matthias, and Aberer Karl . 2015. Result Selection and Summarization for Web Table Search ISDE '15. 425--441.

[20]

Rakesh Pimplikar and Sunita Sarawagi . 2012. Answering Table Queries on the Web Using Column Keywords. Proc. of VLDB Endow. Vol. 5 (2012), 908--919.

Digital Library

[21]

Pedro Saleiro, Natasa Milic-Frayling, Eduarda Mendes Rodrigues, and Carlos Soares . 2017. RELink: A Research Framework and Test Collection for Entity-Relationship Retrieval Proc. of SIGIR '17. 1273--1276.

Digital Library

[22]

Sunita Sarawagi and Soumen Chakrabarti . 2014. Open-domain Quantity Queries on Web Tables: Annotation, Response, and Consensus Models Proc. of KDD '14. 711--720.

Digital Library

[23]

Yoones A. Sekhavat, Francesco Di Paolo, Denilson Barbosa, and Paolo Merialdo . 2014. Knowledge Base Augmentation using Tabular Data. In Proc. of LDOW '14.

[24]

Huan Sun, Hao Ma, Xiaodong He, Wen-tau Yih, Yu Su, and Xifeng Yan . 2016. Table Cell Search for Question Answering. In Proc. of WWW '16. 771--782.

Digital Library

[25]

Petros Venetis, Alon Halevy, Jayant Madhavan, Marius Pacsca, Warren Shen, Fei Wu, Gengxin Miao, and Chung Wu . 2011. Recovering Semantics of Tables on the Web. Proc. of VLDB Endow. Vol. 4 (2011), 528--538.

Digital Library

[26]

Jiannan Wang, Guoliang Li, and Jianhua Feng . 2014. Extending String Similarity Join to Tolerant Fuzzy Token Matching. ACM Trans. Database Syst. Vol. 39, 1 (2014), 1--45.

Digital Library

[27]

Yalin Wang and Jianying Hu . 2002 a. Detecting Tables in HTML Documents. In Proc. of DAS '02. 249--260.

Digital Library

[28]

Yalin Wang and Jianying Hu . 2002 b. A Machine Learning Based Approach for Table Detection on the Web Proc. of WWW '02. 242--250.

Digital Library

[29]

Mohamed Yahya, Klaus Berberich, Shady Elbassuoni, Maya Ramanath, Volker Tresp, and Gerhard Weikum . 2012. Natural Language Questions for the Web of Data. Proc. of EMNLP-CoNLL '12. 379--390.

Digital Library

[30]

Mohamed Yakout, Kris Ganjam, Kaushik Chakrabarti, and Surajit Chaudhuri . 2012. InfoGather: Entity Augmentation and Attribute Discovery by Holistic Matching with Web Tables Proc. of SIGMOD '12. 97--108.

Digital Library

[31]

Mohan Yang, Bolin Ding, Surajit Chaudhuri, and Kaushik Chakrabarti . 2014. Finding Patterns in a Knowledge Base Using Keywords to Compose Table Answers. Proc. VLDB Endow., Vol. 7, 14 (2014), 1809--1820.

Digital Library

[32]

Pengcheng Yin, Zhengdong Lu, Hang Li, and Ben Kao . 2016. Neural Enquirer: Learning to Query Tables in Natural Language Proc. of IJCAI '16. 2308--2314.

Digital Library

[33]

Shuo Zhang, Vugar Abdulzada, and Krisztian Balog . 2018. SmartTable: A Spreadsheet Program with Intelligent Assistance Proc. of SIGIR '18.

Digital Library

[34]

Shuo Zhang and Krisztian Balog . 2017 a. Design Patterns for Fusion-Based Object Retrieval. Proc. of ECIR '17. Springer, 684--690.

[35]

Shuo Zhang and Krisztian Balog . 2017 b. EntiTables: Smart Assistance for Entity-Focused Tables Proc. of SIGIR '17. 255--264.

Digital Library

[36]

Shuo Zhang and Krisztian Balog . 2018. Ad Hoc Table Retrieval using Semantic Similarity. Proceedings of The Web Conference 2018 (WWW '18).

Digital Library

[37]

Stefan Zwicklbauer, Christoph Einsiedler, Michael Granitzer, and Christin Seifert . 2013. Towards Disambiguating Web Tables. In Proc. of ISWC-PD' 13. 205--208.

Digital Library

Cited By

Ye YHui BYang MLi BHuang FLi YChen HDuh WHuang HKato MMothe JPoblete B(2023)Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based ReasoningProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591708(174-184)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591708
Qiu JSong AJin JZhang TDing JFang XQian J(2023)Dependency-Aware Core Column Discovery for Table UnderstandingThe Semantic Web – ISWC 202310.1007/978-3-031-47240-4_9(159-178)Online publication date: 27-Oct-2023
https://doi.org/10.1007/978-3-031-47240-4_9
Xia SAnzum NSalihoglu SZhao JKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)KTabulator: Interactive Ad hoc Table Creation using Knowledge GraphsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445227(1-14)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445227
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Index Terms

On-the-fly Table Generation
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

June 2018

1509 pages

ISBN:9781450356572

DOI:10.1145/3209978

General Chairs:
Kevyn Collins-Thompson
University of Michigan, United States
,
Qiaozhu Mei
University of Michigan, United States
,
Program Chairs:
Brian Davison
Lehigh University, United States
,
Yiqun Liu
Tsinghua University, China
,
Emine Yilmaz
University College London, United Kingdom

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2018

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SIGIR '18

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SIGIR

SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval

July 8 - 12, 2018

MI, Ann Arbor, USA

Acceptance Rates

SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Ye YHui BYang MLi BHuang FLi YChen HDuh WHuang HKato MMothe JPoblete B(2023)Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based ReasoningProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591708(174-184)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591708
Qiu JSong AJin JZhang TDing JFang XQian J(2023)Dependency-Aware Core Column Discovery for Table UnderstandingThe Semantic Web – ISWC 202310.1007/978-3-031-47240-4_9(159-178)Online publication date: 27-Oct-2023
https://doi.org/10.1007/978-3-031-47240-4_9
Xia SAnzum NSalihoglu SZhao JKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)KTabulator: Interactive Ad hoc Table Creation using Knowledge GraphsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445227(1-14)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445227
Li HHu YCao YZhou GLuo P(2021)Rich-text document styling restoration via reinforcement learningFrontiers of Computer Science10.1007/s11704-020-9322-715:4Online publication date: 25-May-2021
https://doi.org/10.1007/s11704-020-9322-7
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
Macdonald EBarbosa Dd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Neural Relation Extraction on Wikipedia Tables for Augmenting Knowledge GraphsProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412164(2133-2136)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412164
Zhang SBalog KZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Auto-completion for Data Cells in Relational TablesProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357932(761-770)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3357932
Zhang SBalog KPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Web Table Extraction, Retrieval and AugmentationProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331385(1409-1410)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331385
Zhang LZhang SBalog KPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Table2VecProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331333(1029-1032)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331333
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