research-article

Semi-supervised truth discovery

Authors:

Wenzhao TanAuthors Info & Claims

WWW '11: Proceedings of the 20th international conference on World wide web

Pages 217 - 226

https://doi.org/10.1145/1963405.1963439

Published: 28 March 2011 Publication History

Abstract

Accessing online information from various data sources has become a necessary part of our everyday life. Unfortunately such information is not always trustworthy, as different sources are of very different qualities and often provide inaccurate and conflicting information. Existing approaches attack this problem using unsupervised learning methods, and try to infer the confidence of the data value and trustworthiness of each source from each other by assuming values provided by more sources are more accurate. However, because false values can be widespread through copying among different sources and out-of-date data often overwhelm up-to-date data, such bootstrapping methods are often ineffective.

In this paper we propose a semi-supervised approach that finds true values with the help of ground truth data. Such ground truth data, even in very small amount, can greatly help us identify trustworthy data sources. Unlike existing studies that only provide iterative algorithms, we derive the optimal solution to our problem and provide an iterative algorithm that converges to it. Experiments show our method achieves higher accuracy than existing approaches, and it can be applied on very huge data sets when implemented with MapReduce.

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Cited By

Wang PJiao DYang LWang BYu R(2024)Hypergraph-based Truth Discovery for Sparse Data in Mobile CrowdsensingACM Transactions on Sensor Networks10.1145/364989420:3(1-23)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3649894
Heidari AMichalopoulos GIlyas IRekatsinas T(2024)Record Fusion via Inference and Data AugmentationACM / IMS Journal of Data Science10.1145/35935791:1(1-23)Online publication date: 16-Jan-2024
https://dl.acm.org/doi/10.1145/3593579
Yazdinejad ADehghantanha ASrivastava GKarimipour HParizi R(2024)Hybrid privacy preserving federated learning against irregular users in Next-generation Internet of ThingsJournal of Systems Architecture10.1016/j.sysarc.2024.103088(103088)Online publication date: Feb-2024
https://doi.org/10.1016/j.sysarc.2024.103088
Show More Cited By

Semi-supervised truth discovery
1. Information systems

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Published In

cover image ACM Other conferences

WWW '11: Proceedings of the 20th international conference on World wide web

March 2011

840 pages

ISBN:9781450306324

DOI:10.1145/1963405

General Chairs:
S. Sadagopan
IIIT-Bangalore, India
,
Krithi Ramamritham
IIT-Bombay, India
,
Arun Kumar
IBM Research, India
,
M. P. Ravindra
Infosys E & R, India
,
Program Chairs:
Elisa Bertino
Purdue University, USA
,
Ravi Kumar
Yahoo! Research, USA

Copyright © 2011 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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
The International Institute of Information Technology Bangalore: The International Institute of Information Technology Bangalore

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2011

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WWW '11

WWW '11: 20th International World Wide Web Conference

March 28 - April 1, 2011

Hyderabad, India

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Cited By

Wang PJiao DYang LWang BYu R(2024)Hypergraph-based Truth Discovery for Sparse Data in Mobile CrowdsensingACM Transactions on Sensor Networks10.1145/364989420:3(1-23)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3649894
Heidari AMichalopoulos GIlyas IRekatsinas T(2024)Record Fusion via Inference and Data AugmentationACM / IMS Journal of Data Science10.1145/35935791:1(1-23)Online publication date: 16-Jan-2024
https://dl.acm.org/doi/10.1145/3593579
Yazdinejad ADehghantanha ASrivastava GKarimipour HParizi R(2024)Hybrid privacy preserving federated learning against irregular users in Next-generation Internet of ThingsJournal of Systems Architecture10.1016/j.sysarc.2024.103088(103088)Online publication date: Feb-2024
https://doi.org/10.1016/j.sysarc.2024.103088
Bornemann LBleifuß TKalashnikov DNargesian FNaumann FSrivastava D(2023)Matching Roles from Temporal Data: Why Joe Biden is not only President, but also Commander-in-ChiefProceedings of the ACM on Management of Data10.1145/35889191:1(1-26)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588919
Sukprasert PChan GRossi RDu FKoh E(2023)Discovery and Matching Numerical Attributes in Data Lakes2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386080(423-432)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386080
Zhang HLi M(2022)Multi-round Data Poisoning Attack and Defense against Truth Discovery in Crowdsensing Systems2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00036(109-118)Online publication date: Jun-2022
https://doi.org/10.1109/MDM55031.2022.00036
Yan LYang KYang S(2022)Reputation-Based Truth Discovery With Long-Term Quality of Source in Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2021.31105119:7(5410-5421)Online publication date: 1-Apr-2022
https://doi.org/10.1109/JIOT.2021.3110511
Singleton JBooth R(2022)Towards an axiomatic approach to truth discoveryAutonomous Agents and Multi-Agent Systems10.1007/s10458-022-09569-336:2Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10458-022-09569-3
Wongchaisuwat PKlabjan D(2022)Truth validation with evidenceKnowledge and Information Systems10.1007/s10115-022-01663-y64:5(1187-1209)Online publication date: 15-Mar-2022
https://doi.org/10.1007/s10115-022-01663-y
Azzalini FPiantella DRabosio ETanca L(2022)Enhancing domain-aware multi-truth data fusion using copy-based source authority and value similarityThe VLDB Journal10.1007/s00778-022-00757-x32:3(475-500)Online publication date: 19-Jul-2022
https://doi.org/10.1007/s00778-022-00757-x
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