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| Content Provider | ACM Digital Library |
|---|---|
| Author | Pochampally, Ravali Das Sarma, Anish Meliou, Alexandra Srivastava, Divesh Dong, Xin Luna |
| Abstract | Many applications rely on Web data and extraction systems to accomplish knowledge-driven tasks. Web information is not curated, so many sources provide inaccurate, or conflicting information. Moreover, extraction systems introduce additional noise to the data. We wish to automatically distinguish correct data and erroneous data for creating a cleaner set of integrated data. Previous work has shown that a naive voting strategy that trusts data provided by the majority or at least a certain number of sources may not work well in the presence of copying between the sources. However, correlation between sources can be much broader than copying: sources may provide data from complementary domains (negative correlation), extractors may focus on different types of information (negative correlation), and extractors may apply common rules in extraction (positive correlation, without copying). In this paper we present novel techniques modeling correlations between sources and applying it in truth finding. We provide a comprehensive evaluation of our approach on three real-world datasets with different characteristics, as well as on synthetic data, showing that our algorithms outperform the existing state-of-the-art techniques. |
| Starting Page | 433 |
| Ending Page | 444 |
| Page Count | 12 |
| File Format | |
| ISBN | 9781450323765 |
| DOI | 10.1145/2588555.2593674 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-06-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Integration Correlated sources Data fusion |
| Content Type | Text |
| Resource Type | Article |
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