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Probe, Count, and Classify: Categorizing Hidden-Web Databases

Summary: Automates hidden-web database categorization with a small set of query probes; uses per-probe match counts, no page retrieval. Evaluated on 100+ real databases; achieves low overhead and high accuracy for automatic hierarchical categorization. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3320
Venue
SIGMOD
Year
2001
Pagerank
6.6457031e-05
Overall Rank
4,524 | 68.97%
DOI
10.1145/375663.375671

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{ipeirotis_sigmod01,
        title = {{Probe, Count, and Classify: Categorizing Hidden-Web Databases}},
        author = {Ipeirotis, Panagiotis G. and Gravano, Luis and Sahami, Mehran},
        series = {{SIGMOD} '01},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/375663.375671},
        url = {https://dl.acm.org/doi/10.1145/375663.375671},
        year = {2001}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,601 Automatic Discovery of Language Models for Text Databases 1999 SIGMOD 0.00010239819
1,625 Determining Text Databases to Search in the Internet 1998 VLDB 0.00010191388
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