The DOI matches its Crossref record.
Accelerated focused crawling through online relevance feedback
Abstract
The organization of HTML into a tag tree structure, which is rendered by browsers as roughly rectangular regions with embedded text and HREF links, greatly helps surfers locate and click on links that best satisfy their information need. Can an automatic program emulate this human behavior and thereby learn to predict the relevance of an unseen HREF target page w.r.t. an information need, based on information limited to the HREF source page? Such a capability would be of great interest in focused crawling and resource discovery, because it can fine-tune the priority of unvisited URLs in the crawl frontier, and reduce the number of irrelevant pages which are fetched and discarded.
Overview
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Citations
Cited by
Sarawagi, S. (2008) · Foundations and Trends in Databases · doi:10.1561/1900000003 · Cited by 707
Analysis of Hacking Related Trade in the Darkweb
Cherqi, O., Mezzour, G., Ghogho, M., & El Koutbi, M. (2018) · 2018 IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2018.8587311 · Cited by 24
Metadata
- Authors
- Chakrabarti, S., Punera, K., & Subramanyam, M.
- Venue
- Proceedings of the 11th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2002-05-07
- Pages
- 148-159
- OpenAlex
- W2017726337
- Cited by
- 232
- References
- 0
- FWCI
- 11.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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How to cite
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- CoChat. (2026). Citation check: Accelerated focused crawling through online relevance feedback. https://app.cochat.ai/papers/10.1145/511446.511466
- The paper
- Chakrabarti, S., Punera, K., & Subramanyam, M. (2002). Accelerated focused crawling through online relevance feedback. Proceedings of the 11th international conference on World Wide Web. https://doi.org/10.1145/511446.511466
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
