Abstract
An increasing number of web sites offer structured information about recognizable concepts, relevant to many application domains, such as finance, sport, commercial products. However, web data is inherently imprecise and uncertain, and conflicting values can be provided by different web sources. Characterizing the uncertainty of web data represents an important issue and several models have been recently proposed in the literature. This chapter illustrates state-of-the-art Bayesan models to evaluate the quality of data extracted from the Web and reports the results of an extensive application of the models on real life web data. Experimental results show that for some applications even simple approaches can provide effective results, while sophisticated solutions are needed to obtain a more precise characterization of the uncertainty.
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References
Amento, B., Terveen, L.G., Hill, W.C.: Does “authority” mean quality? predicting expert quality ratings of web documents. In: SIGIR, pp. 296–303 (2000)
Batini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies, and Techniques. Springer (2008)
Blanco, L., Bronzi, M., Crescenzi, V., Merialdo, P., Papotti, P.: Exploiting information redundancy to wring out structured data from the web. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010. ACM, New York (2010)
Blanco, L., Bronzi, M., Crescenzi, V., Merialdo, P., Papotti, P.: Redundancy-driven web data extraction and integration. In: WebDB (2010)
Blanco, L., Crescenzi, V., Merialdo, P., Papotti, P.: Probabilistic Models to Reconcile Complex Data from Inaccurate Data Sources. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 83–97. Springer, Heidelberg (2010)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks 30(1-7), 107–117 (1998)
Cafarella, M.J., Etzioni, O., Suciu, D.: Structured queries over web text. IEEE Data Eng. Bull. 29(4), 45–51 (2006)
Clemen, R.T., Winkler, R.L.: Combining probability distributions from experts in risk analysis. Risk Analysis 19(2), 187–203 (1999)
Dalvi, N.N., Suciu, D.: Management of probabilistic data: foundations and challenges. In: PODS, pp. 1–12 (2007)
Dong, X., Berti-Equille, L., Hu, Y., Srivastava, D.: Global detection of complex copying relationships between sources. PVLDB 3(1), 1358–1369 (2010)
Dong, X.L., Berti-Equille, L., Srivastava, D.: Integrating conflicting data: The role of source dependence. PVLDB 2(1), 550–561 (2009)
Dong, X.L., Berti-Equille, L., Srivastava, D.: Truth discovery and copying detection in a dynamic world. PVLDB 2(1), 562–573 (2009)
Downey, D., Etzioni, O., Soderland, S.: A probabilistic model of redundancy in information extraction. In: IJCAI, pp. 1034–1041 (2005)
Florescu, D., Koller, D., Levy, A.Y.: Using probabilistic information in data integration. In: VLDB, pp. 216–225 (1997)
Galland, A., Abiteboul, S., Marian, A., Senellart, P.: Corroborating information from disagreeing views. In: Proc. WSDM, New York, USA (2010)
Pal, A., Rastogi, V., Machanavajjhala, A., Bohannon, P.: Information integration over time in unreliable and uncertain environments. In: WWW, pp. 789–798 (2012)
Wu, M., Marian, A.: Corroborating answers from multiple web sources. In: WebDB (2007)
Yin, X., Han, J., Yu, P.S.: Truth discovery with multiple conflicting information providers on the web. IEEE Trans. Knowl. Data Eng. 20(6), 796–808 (2008)
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Blanco, L., Crescenzi, V., Merialdo, P., Papotti, P. (2012). Web Data Reconciliation: Models and Experiences. In: Ceri, S., Brambilla, M. (eds) Search Computing. Lecture Notes in Computer Science, vol 7538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34213-4_1
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DOI: https://doi.org/10.1007/978-3-642-34213-4_1
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