Abstract
Data fusion is a fundamental research problem of identifying true values of data items of interest from conflicting multi-sourced data. Although considerable research efforts have been conducted on this topic, existing approaches generally assume every data item has exactly one true value, which fails to reflect the real world where data items with multiple true values widely exist. In this paper, we propose a novel approach, SourceVote, to estimate value veracity for multi-valued data items. SourceVote models the endorsement relations among sources by quantifying their two-sided inter-source agreements. In particular, two graphs are constructed to model inter-source relations. Then two aspects of source reliability are derived from these graphs and are used for estimating value veracity and initializing existing data fusion methods. Empirical studies on two large real-world datasets demonstrate the effectiveness of our approach.
The original version of this chapter was revised: Acknowledgment section has been added in the chapter. The correction to this chapter is available at https://doi.org/10.1007/978-3-319-69904-2_40
This is a preview of subscription content, log in via an institution.
Change history
21 November 2018
The original version of the chapter was incomplete. The complete information must be read as follows: This work was supported in part (for the co-author Mahmoud Barhamgi) by the Justice Programme of the European Union (2014-2020) 723180, RiskTrack, under Grant JUST-2015-JCOO-AG and Grant JUST-2015-JCOO-AG-1.
Notes
- 1.
Here we neglect the smoothing links, i.e., no link would be there between two sources in the graphs if no common value exists between the two sources.
- 2.
Note that we did not apply SourceVote to Voting, because Voting assumes all sources are equally reliable.
References
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)
Dong, X.L., et al.: Less is more: selecting sources wisely for integration. VLDB Endow. (PVLDB) 6(2), 37–48 (2013)
Dong, X.L., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014), New York, USA (2014)
Galland, A., et al.: Corroborating information from disagreeing views. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM 2010), New York, USA (2010)
Gleich, D.F., et al.: Tracking the random surfer: empirically measured teleportation parameters in pagerank. In: Proceedings of the 19th International World Wide Web Conference (WWW 2010), Raleigh, NC, USA (2010)
Kleinberg, J.: Authoritative sources in a hyper-linked environment. J. ACM 46(5), 604–632 (1999)
Li, X., et al.: Truth finding on the deep web: is the problem solved? VLDB Endow. (PVLDB) 6(2), 97–108 (2013)
Li, Y., Gao, J., Meng, C., Li, Q., Su, L., Zhao, B., Fan, W., Han, J.: A survey on truth discovery. ACM SIGKDD Explor. Newsl. 17(2), 1–16 (2015)
Pasternack, J., Roth, D.: Knowing what to believe (when you already know something). In: Proceedings of the 23th International Conference on Computational Linguistics (COLING 2010), Stroudsburg, PA, USA (2010)
Waguih, D.A., Berti-Equille, L.: Truth discovery algorithms: an experimental evaluation. arXiv preprint (2014). arXiv:1409.6428
Wang, X., et al.: An integrated Bayesian approach for effective multi-truth discovery. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM 2015), Melbourne, Australia (2015)
Wang, X., et al.: Empowering truth discovery with multi-truth prediction. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), pp. 881–890 (2016)
Wang, X., et al.: Truth discovery via exploiting implications from multi-source data. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), pp. 861–870 (2016)
Yin, X., et al.: Truth discovery with multiple conflicting information providers on the web. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), San Jose, California, USA (2007)
Zhao, B., et al.: A Bayesian approach to discovering truth from conflicting sources for data integration. The VLDB Endow. (PVLDB) 5(6), 550–561 (2012)
Acknowledgment
This work was supported in part (for the co-author Mahmoud Barhamgi) by the Justice Programme of the European Union (2014-2020) 723180, RiskTrack, under Grant JUST-2015-JCOO-AG and Grant JUST-2015-JCOO-AG-1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Fang, X.S., Sheng, Q.Z., Wang, X., Barhamgi, M., Yao, L., Ngu, A.H.H. (2017). SourceVote: Fusing Multi-valued Data via Inter-source Agreements. In: Mayr, H., Guizzardi, G., Ma, H., Pastor, O. (eds) Conceptual Modeling. ER 2017. Lecture Notes in Computer Science(), vol 10650. Springer, Cham. https://doi.org/10.1007/978-3-319-69904-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-69904-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69903-5
Online ISBN: 978-3-319-69904-2
eBook Packages: Computer ScienceComputer Science (R0)