Fusing Data with Correlations
Summary: Models correlations among sources beyond simple copying (positive/negative, cross-domain, extractor rules) to improve truth discovery in web-harvested data. Evaluated on three real/synthetic datasets, it outperforms state-of-the-art methods by robustly fusing noisy, conflicting web data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ravali Pochampally (University of Massachusetts Amherst)
- 2. Anish Das Sarma (Troo.Ly Inc.)
- 3. Xin Luna Dong (Google)
- 4. Alexandra Meliou (University of Massachusetts Amherst)
- 5. Divesh Srivastava (AT&T)
BibTeX Citation
@inproceedings{pochampally_sigmod14,
title = {{Fusing Data with Correlations}},
author = {Pochampally, Ravali and Sarma, Anish Das and Dong, Xin Luna and Meliou, Alexandra and Srivastava, Divesh},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2593764},
url = {https://dl.acm.org/doi/10.1145/2588555.2593764},
year = {2014}
}
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