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Characterizing the uncertainty of web data: models and experiences
| Content Provider | ACM Digital Library |
|---|---|
| Author | Merialdo, Paolo Papotti, Paolo Crescenzi, Valter Blanco, Lorenzo |
| 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. The paper 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. Our 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. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450307062 |
| DOI | 10.1145/1964114.1964116 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-03-28 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Probabilistic data Web data extraction Data reconciliation |
| Content Type | Text |
| Resource Type | Article |
