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A Survey on Truth Discovery

Authors:

Jiawei HanAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 17, Issue 2

Pages 1 - 16

https://doi.org/10.1145/2897350.2897352

Published: 25 February 2016 Publication History

Abstract

Thanks to information explosion, data for the objects of interest can be collected from increasingly more sources. However, for the same object, there usually exist conflicts among the collected multi-source information. To tackle this challenge, truth discovery, which integrates multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. Several truth discovery methods have been proposed for various scenarios, and they have been successfully applied in diverse application domains. In this survey, we focus on providing a comprehensive overview of truth discovery methods, and summarizing them from different aspects. We also discuss some future directions of truth discovery research. We hope that this survey will promote a better understanding of the current progress on truth discovery, and offer some guidelines on how to apply these approaches in application domains.

References

[1]

Amazon mechanical turk. https://www.mturk.com/mturk/welcome.

[2]

Freebase. https://www.freebase.com/.

[3]

Google knowledge graph. http://www.google.com/insidesearch/features/search/knowledge.html.

[4]

Yago. http://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/.

[5]

C. C. Aggarwal and T. Abdelzaher. Social sensing. In Managing and mining sensor data, pages 237--297. 2013.

[6]

B. Aydin, Y. Yilmaz, Y. Li, Q. Li, J. Gao, and M. Demirbas. Crowdsourcing for multiple-choice question answering. In Proc. of the Conference on Innovative Applications of Artificial Intelligence (IAAI'14), pages 2946--2953, 2014.

Digital Library

[7]

D. P. Bertsekas. Non-linear Programming. Athena Scientific, 2nd edition, 1999.

[8]

S. Bickel and T. Scheffer. Multi-view clustering. In Proc. of the IEEE International Conference on Data Mining (ICDM'04), pages 19--26, 2004.

Digital Library

[9]

J. Bleiholder and F. Naumann. Conflict handling strategies in an integrated information system. In Proc. of the International Workshop on Information Integration on the Web (IIWeb'06), 2006.

[10]

J. Bleiholder and F. Naumann. Data fusion. ACM Computing Surveys, 41(1):1:1--1:41, 2009.

Digital Library

[11]

A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proc. of the annual conference on Computational learning theory (COLT'98), pages 92--100, 1998.

Digital Library

[12]

A. P. Dawid and A. M. Skene. Maximum likelihood estimation of observer error-rates using the em algorithm. Applied statistics, pages 20--28, 1979.

[13]

R. DerSimonian and N. Laird. Meta-analysis in clinical trials. Controlled clinical trials, 7(3):177--188, 1986.

[14]

X. Dong, L. Berti-Equille, Y. Hu, and D. Srivastava. Global detection of complex copying relationships between sources. PVLDB, 3(1):1358--1369, 2010.

Digital Library

[15]

X. L. Dong, L. Berti-Equille, and D. Srivastava. Integrating conflicting data: The role of source dependence. PVLDB, 2(1):550--561, 2009.

Digital Library

[16]

X. L. Dong, L. Berti-Equille, and D. Srivastava. Truth discovery and copying detection in a dynamic world. PVLDB, 2(1):562--573, 2009.

Digital Library

[17]

X. L. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), pages 601--610, 2014.

Digital Library

[18]

X. L. Dong, E. Gabrilovich, G. Heitz, W. Horn, K. Murphy, S. Sun, and W. Zhang. From data fusion to knowledge fusion. PVLDB, 7(10):881--892, 2014.

Digital Library

[19]

X. L. Dong, E. Gabrilovich, K. Murphy, V. Dang, W. Horn, C. Lugaresi, S. Sun, and W. Zhang. Knowledge-based trust: Estimating the trustworthiness of web sources. PVLDB, 8(9):938--949, 2015.

Digital Library

[20]

X. L. Dong and F. Naumann. Data fusion: Resolving data conflicts for integration. PVLDB, 2(2):1654--1655, 2009.

Digital Library

[21]

X. L. Dong, B. Saha, and D. Srivastava. Less is more: Selecting sources wisely for integration. PVLDB, 6(2):37--48, 2012.

Digital Library

[22]

X. L. Dong and D. Srivastava. Compact explanation of data fusion decisions. In Proc. of the International Conference on World Wide Web (WWW'13), pages 379--390, 2013.

Digital Library

[23]

C. Dwork, R. Kumar, M. Naor, and D. Sivakumar. Rank aggregation methods for the web. In Proc. of the International Conference on World Wide Web (WWW'01), pages 613--622, 2001.

Digital Library

[24]

A. Galland, S. Abiteboul, A. Marian, and P. Senellart. Corroborating information from disagreeing views. In Proc. of the ACM International Conference on Web Search and Data Mining (WSDM'10), pages 131--140, 2010.

Digital Library

[25]

M. Gupta, Y. Sun, and J. Han. Trust analysis with clustering. In Proc. of the International Conference on World Wide Web (WWW'11), pages 53--54, 2011.

Digital Library

[26]

H. Le, D. Wang, H. Ahmadi, Y. S. Uddin, B. Szymanski, R. Ganti, and T. Abdelzaher. Demo: Distilling likely truth from noisy streaming data with apollo. In Proc. of the ACM International Conference on Embedded Networked Sensor Systems (Sensys'11), pages 417--418, 2011.

Digital Library

[27]

F. Li, M. L. Lee, and W. Hsu. Entity profiling with varying source reliabilities. In Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), pages 1146--1155, 2014.

Digital Library

[28]

H. Li, B. Zhao, and A. Fuxman. The wisdom of minority: discovering and targeting the right group of workers for crowdsourcing. In Proc. of the International Conference on World Wide Web (WWW'14), pages 165--176, 2014.

Digital Library

[29]

Q. Li, Y. Li, J. Gao, L. Su, B. Zhao, D. Murat, W. Fan, and J. Han. A confidence-aware approach for truth discovery on long-tail data. PVLDB, 8(4):425--436, 2015.

Digital Library

[30]

Q. Li, Y. Li, J. Gao, B. Zhao, W. Fan, and J. Han. Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. In Proc. of the ACM SIGMOD International Conference on Management of Data (SIGMOD'14), pages 1187--1198, 2014.

Digital Library

[31]

X. Li, X. L. Dong, K. B. Lyons, W. Meng, and D. Srivastava. Truth finding on the deep web: Is the problem solved? PVLDB, 6(2):97--108, 2012.

Digital Library

[32]

Y. Li, Q. Li, J. Gao, L. Su, B. Zhao, W. Fan, and J. Han. On the discovery of evolving truth. In Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'15), pages 675--684, 2015.

Digital Library

[33]

S. Lin. Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5):555--570, 2010.

Digital Library

[34]

M. W. Lipsey and D. B. Wilson. Practical metaanalysis, volume 49. 2001.

[35]

X. Liu, X. L. Dong, B. C. Ooi, and D. Srivastava. Online data fusion. PVLDB, 4(11):932--943, 2011.

Digital Library

[36]

R. C. Luo, C.-C. Yih, and K. L. Su. Multisensor fusion and integration: approaches, applications, and future research directions. IEEE Sensors Journal, 2(2):107--119, 2002.

[37]

F. Ma, Y. Li, Q. Li, M. Qiu, J. Gao, S. Zhi, L. Su, B. Zhao, H. Ji, and J. Han. Faitcrowd: Fine grained truth discovery for crowdsourced data aggregation. In Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'15), pages 745--754, 2015.

Digital Library

[38]

A. Marian and M. Wu. Corroborating information from web sources. IEEE Data Engineering Bulletin, 34(3):11--17, 2011.

[39]

C. Meng, W. Jiang, Y. Li, J. Gao, L. Su, H. Ding, and Y. Cheng. Truth discovery on crowd sensing of correlated entities. In Proc. of the ACM International Conference on Embedded Networked Sensor Systems (Sensys'15), 2015.

Digital Library

[40]

C. Miao, W. Jiang, L. Su, Y. Li, S. Guo, Z. Qin, H. Xiao, J. Gao, and K. Ren. Cloud-enabled privacypreserving truth discovery in crowd sensing systems. In Proc. of the ACM International Conference on Embedded Networked Sensor Systems (Sensys'15), 2015.

Digital Library

[41]

H. B. Mitchell. Multi-sensor data fusion: an introduction. Springer Science & Business Media, 2007.

Digital Library

[42]

S. Mukherjee, G. Weikum, and C. Danescu-Niculescu- Mizil. People on drugs: credibility of user statements in health communities. In Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), pages 65--74, 2014.

Digital Library

[43]

V.-A. Nguyen, E.-P. Lim, J. Jiang, and A. Sun. To trust or not to trust? predicting online trusts using trust antecedent framework. In Proc. of the IEEE International Conference on Data Mining (ICDM'09), pages 896--901, 2009.

Digital Library

[44]

J. O'Donovan and B. Smyth. Trust in recommender systems. In Proc. of the international conference on Intelligent user interfaces (IUI'05), pages 167--174, 2005.

Digital Library

[45]

J. Pasternack and D. Roth. Comprehensive trust metrics for information networks. In Army Science Conference, 2010.

[46]

J. Pasternack and D. Roth. Knowing what to believe (when you already know something). In Proc. of the International Conference on Computational Linguistics (COLING'10), pages 877--885, 2010.

Digital Library

[47]

J. Pasternack and D. Roth. Making better informed trust decisions with generalized fact-finding. In Proc. of the International Jont Conference on Artifical Intelligence (IJCAI'11), pages 2324--2329, 2011.

Digital Library

[48]

J. Pasternack and D. Roth. Latent credibility analysis. In Proc. of the International Conference on World Wide Web (WWW'13), pages 1009--1020, 2013.

Digital Library

[49]

R. Pochampally, A. D. Sarma, X. L. Dong, A. Meliou, and D. Srivastava. Fusing data with correlations. In Proc. of the ACM SIGMOD International Conference on Management of Data (SIGMOD'14), pages 433--444, 2014.

Digital Library

[50]

G.-J. Qi, C. C. Aggarwal, J. Han, and T. Huang. Mining collective intelligence in diverse groups. In Proc. of the International Conference on World Wide Web (WWW'13), pages 1041--1052, 2013.

Digital Library

[51]

V. C. Raykar, S. Yu, L. H. Zhao, A. Jerebko, C. Florin, G. H. Valadez, L. Bogoni, and L. Moy. Supervised learning from multiple experts: Whom to trust when everyone lies a bit. In Proc. of the International Conference on Machine Learning (ICML'09), pages 889--896, 2009.

Digital Library

[52]

T. Rekatsinas, X. L. Dong, and D. Srivastava. Characterizing and selecting fresh data sources. In Proc. of the ACM SIGMOD International Conference on Management of Data (SIGMOD'14), pages 919--930, 2014.

Digital Library

[53]

A. D. Sarma, X. L. Dong, and A. Halevy. Data integration with dependent sources. In Proc. of the International Conference on Extending Database Technology (EDBT'11), pages 401--412, 2011.

Digital Library

[54]

G. Seni and J. F. Elder. Ensemble methods in data mining: improving accuracy through combining predictions. nSynthesis Lectures on Data Mining and Knowledge Discovery, 2(1):1--126, 2010.

Digital Library

[55]

V. S. Sheng, F. Provost, and P. G. Ipeirotis. Get another label? improving data quality and data mining using multiple, noisy labels. In Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08), pages 614--622, 2008.

Digital Library

[56]

P. Smyth, U. Fayyad, M. Burl, P. Perona, and P. Baldi. Inferring ground truth from subjective labelling of venus images. In Advances in Neural Information Processing Systems (NIPS'95), pages 1085--1092, 1995.

[57]

R. Snow, B. O'Connor, D. Jurafsky, and A. Ng. Cheap and fast - but is it good? evaluating non-expert annotations for natural language tasks. In Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP'08), pages 254--263, 2008.

Digital Library

[58]

A. Sorokin and D. Forsyth. Utility data annotation with amazon mechanical turk. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'08), pages 1--8, 2008.

[59]

M. Spain and P. Perona. Some objects are more equal than others: Measuring and predicting importance. In Proc. European Conference on Computer Vision (ECCV'08), pages 523--536, 2008.

Digital Library

[60]

L. Su, Q. Li, S. Hu, S. Wang, J. Gao, H. Liu, T. Abdelzaher, J. Han, X. Liu, Y. Gao, and L. Kaplan. Generalized decision aggregation in distributed sensing systems. In Proc. of the IEEE Real-Time Systems Symposium (RTSS'14), pages 1--10, 2014.

[61]

J. Tang and H. Liu. Trust in social computing. In Proc. of the international conference on World wide web companion, pages 207--208, 2014.

Digital Library

[62]

L.-A. Tang, X. Yu, S. Kim, Q. Gu, J. Han, A. Leung, and T. La Porta. Trustworthiness analysis of sensor data in cyber-physical systems. Journal of Computer and System Sciences, 79(3):383--401, 2013.

Digital Library

[63]

P. Victor, M. De Cock, and C. Cornelis. Trust and recommendations. In Recommender systems handbook, pages 645--675. Springer, 2011.

[64]

D. Wang, T. Abdelzaher, L. Kaplan, and C. Aggarwal. Recursive fact-finding: A streaming approach to truth estimation in crowdsourcing applications. In Proc. of the International Conference on Distributed Computing Systems (ICDCS'13), pages 530--539, 2013.

Digital Library

[65]

D. Wang, M. T. Amin, S. Li, T. Abdelzaher, L. Kaplan, S. Gu, C. Pan, H. Liu, C. C. Aggarwal, R. Ganti, et al. Using humans as sensors: An estimation-theoretic perspective. In Proc. of the International Conference on Information Processing in Sensor Networks (IPSN'14), pages 35--46, 2014.

Digital Library

[66]

D. Wang, L. Kaplan, and T. F. Abdelzaher. Maximum likelihood analysis of conflicting observations in social sensing. ACM Transactions on Sensor Networks (ToSN), 10(2):30, 2014.

Digital Library

[67]

D. Wang, L. Kaplan, H. Le, and T. Abdelzaher. On truth discovery in social sensing: A maximum likelihood estimation approach. In Proc. of the International Conference on Information Processing in Sensor Networks (IPSN'12), pages 233--244, 2012.

Digital Library

[68]

S. Wang, L. Su, S. Li, S. Yao, S. Hu, L. Kaplan, T. Amin, T. Abdelzaher, and W. Hongwei. Scalable social sensing of interdependent phenomena. In Proc. of the International Conference on Information Processing in Sensor Networks (IPSN'15), pages 202--213, 2015.

Digital Library

[69]

S. Wang, D. Wang, L. Su, L. Kaplan, and T. Abdelzaher. Towards cyber-physical systems in social spaces: The data reliability challenge. In Proc. of the IEEE Real-Time Systems Symposium (RTSS'14), pages 74--85, 2014.

[70]

P. Welinder, S. Branson, S. Belongie, and P. Perona. The multidimensional wisdom of crowds. In Advances in Neural Information Processing Systems (NIPS'10), pages 2424--2432, 2010.

[71]

J. Whitehill, P. Ruvolo, T. Wu, J. Bergsma, and J. Movellan. Whose vote should count more: Optimal integration of labelers of unknown expertise. In Advances in Neural Information Processing Systems (NIPS'09), pages 2035--2043, 2009.

[72]

M. Wu and A. Marian. A framework for corroborating answers from multiple web sources. Information Systems, 36(2):431--449, 2011.

Digital Library

[73]

C. Xu, D. Tao, and C. Xu. A survey on multi-view learning. arXiv preprint arXiv:1304.5634, 2013.

[74]

X. Yin, J. Han, and P. S. Yu. Truth discovery with multiple conflicting information providers on the web. In Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'07), pages 1048--1052, 2007.

Digital Library

[75]

X. Yin and W. Tan. Semi-supervised truth discovery. In Proc. of the International Conference on World Wide Web (WWW'11), pages 217--226, 2011.

Digital Library

[76]

D. Yu, H. Huang, T. Cassidy, H. Ji, C. Wang, S. Zhi, J. Han, C. Voss, and M. Magdon-Ismail. The wisdom of minority: Unsupervised slot filling validation based on multi-dimensional truth-finding. In Proc. of the International Conference on Computational Linguistics (COLING'14), 2014.

[77]

B. Zhao and J. Han. A probabilistic model for estimating real-valued truth from conflicting sources. In Proc. of the VLDB workshop on Quality in Databases (QDB'12), 2012.

[78]

B. Zhao, B. I. P. Rubinstein, J. Gemmell, and J. Han. A bayesian approach to discovering truth from conflicting sources for data integration. PVLDB, 5(6):550--561, 2012.

Digital Library

[79]

Z. Zhao, J. Cheng, and W. Ng. Truth discovery in data streams: A single-pass probabilistic approach. In Proc. of the ACM Conference on Information and Knowledge Management (CIKM'14), pages 1589--1598, 2014.

Digital Library

[80]

S. Zhi, B. Zhao, W. Tong, J. Gao, D. Yu, H. Ji, and J. Han. Modeling truth existence in truth discovery. In Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'15), pages 1543--1552, 2015.

Digital Library

[81]

D. Zhou, J. C. Platt, S. Basu, and Y. Mao. Learning from the wisdom of crowds by minimax entropy. In Advances in Neural Information Processing Systems (NIPS'12), pages 2204--2212, 2012.

[82]

Z.-H. Zhou. Ensemble methods: foundations and algorithms. Chapman & Hall/CRC Machine Learning & Pattern Recognition Series, 2012.

Digital Library

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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 17, Issue 2

December 2015

41 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/2897350

Editors:
Charu Aggarwal
IBM T.J. Watson
,
Haixun Wang
Google
,
Ankur M. Teredesai
University of Washington Tacoma
,
Hanghang Tong
Arizona State University

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Copyright © 2016 Authors.

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Published: 25 February 2016

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https://doi.org/10.1016/j.ejor.2024.08.024
Han BFu QSu HChi CZhang CWang J(2024)A Privacy-Preserving and Quality-Aware User Selection Scheme for IoTMathematics10.3390/math1219296112:19(2961)Online publication date: 24-Sep-2024
https://doi.org/10.3390/math12192961
Peng HZhang PTang JXu HZeng W(2024)Detect-Then-Resolve: Enhancing Knowledge Graph Conflict Resolution with Large Language ModelMathematics10.3390/math1215231812:15(2318)Online publication date: 24-Jul-2024
https://doi.org/10.3390/math12152318
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