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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
hed021c348f2da1e8
Venue
SIGMOD
Year
2001
Pagerank
6.4814577e-05
Overall Rank
4,623 | 69.02%
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
1,638 Automatic Discovery of Language Models for Text Databases 1999 SIGMOD 9.9886199e-05
1,663 Determining Text Databases to Search in the Internet 1998 VLDB 9.9393733e-05
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