The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
A Bayesian approach to discovering truth from conflicting sources for data integration
Abstract
In practical data integration systems, it is common for the data sources being integrated to provide conflicting information about the same entity. Consequently, a major challenge for data integration is to derive the most complete and accurate integrated records from diverse and sometimes conflicting sources. We term this challenge the truth finding problem . We observe that some sources are generally more reliable than others, and therefore a good model of source quality is the key to solving the truth finding problem. In this work, we propose a probabilistic graphical model that can automatically infer true records and source quality without any supervision. In contrast to previous methods, our principled approach leverages a generative process of two types of errors (false positive and false negative) by modeling two different aspects of source quality. In so doing, ours is also the first approach designed to merge multi-valued attribute types. Our method is scalable, due to an efficient sampling-based inference algorithm that needs very few iterations in practice and enjoys linear time complexity, with an even faster incremental variant. Experiments on two real world datasets show that our new method outperforms existing state-of-the-art approaches to the truth finding problem.
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Cites
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Integrating conflicting data: the role of source dependence
Dong, X. L., Berti-Equille, L., & Srivastava, D. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687627.1687690 · Cited by 312
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Pasternack, J., & Roth, D. (2010) · Cited by 276
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Self-citationSemi-supervised truth discovery
Yin, X., & Tan, W. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963439 · Cited by 176
Truth discovery and copying detection in a dynamic world
Dong, X. L., Berti-Equille, L., & Srivastava, D. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687627.1687691 · Cited by 146
Using Probabilistic Information in Data Integration
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Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation
Li, Q., Li, Y., Gao, J., Zhao, B., Fan, W., & Han, J. (2014) · Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data · doi:10.1145/2588555.2610509 · Cited by 426
Self-citationLi, Y., Gao, J., Meng, C., Li, Q., Su, L., Zhao, B., Fan, W., & Han, J. (2016) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2897350.2897352 · Cited by 336
Self-citationLeveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
Kim, J., Tabibian, B., Oh, A., Schölkopf, B., & Gomez-Rodriguez, M. (2018) · Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining · doi:10.1145/3159652.3159734 · Cited by 230
Fake News Detection in Social Networks via Crowd Signals
Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2018) · Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18 · doi:10.1145/3184558.3188722 · Cited by 229
Influence-Aware Truth Discovery
Zhang, H., Li, Q., Ma, F., Xiao, H., Li, Y., Gao, J., & Su, L. (2016) · Proceedings of the 25th ACM International on Conference on Information and Knowledge Management · doi:10.1145/2983323.2983785 · Cited by 26
Wu, W., Wang, H., Li, B., Huang, P., Zhao, X., & Liang, L. (2025) · 2025 IEEE 41st International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00230 · Cited by 18
A Survey on Truth Discovery: Concepts, Methods, Applications, and Opportunities
Wang, S., Zhang, H., Sheng, Q. Z., Li, X., Sun, Z., Cai, T., Zhang, W. E., Yang, J., & Gao, Q. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3423677 · Cited by 11
How can ontologies give you clue for truth-discovery? an exploratory study
Beretta, V., Harispe, S., Ranwez, S., & Mougenot, I. (2016) · Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics · doi:10.1145/2912845.2912848 · Cited by 8
Metadata
- Authors
- Zhao, B., Rubinstein, B. I. P., Gemmell, J., & Han, J.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2012-02
- Volume
- 5
- Issue
- 6
- Pages
- 550-561
- ISSN
- 2150-8097
- Cited by
- 264
- References
- 15
- Language
- en
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