The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Effective site finding using link anchor information
Abstract
Link-based ranking methods have been described in the literature and applied in commercial Web search engines. However, according to recent TREC experiments, they are no better than traditional content-based methods. We conduct a different type of experiment, in which the task is to find the main entry point of a specific Web site. In our experiments, ranking based on link anchor text is twice as effective as ranking based on document content, even though both methods used the same BM25 formula. We obtained these results using two sets of 100 queries on a 18.5 million document set and another set of 100 on a 0.4 million document set. This site finding effectiveness begins to explain why many search engines have adopted link methods. It also opens a rich new area for effectiveness improvement, where traditional methods fail.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Cited by
Broder, A. (2002) · ACM SIGIR Forum · doi:10.1145/792550.792552 · Cited by 1,944
Can social bookmarking improve web search?
Heymann, P., Koutrika, G., & Garcia-Molina, H. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341558 · Cited by 434
Barbosa, N. M., Hayes, J., Kaushik, S., & Wang, Y. (2022) · ACM Transactions on Accessible Computing · doi:10.1145/3519032 · Cited by 28
Metadata
- Authors
- Craswell, N., Hawking, D., & Robertson, S.
- Venue
- Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2001-09
- Pages
- 250-257
- OpenAlex
- W2164052363
- Cited by
- 269
- References
- 1
- FWCI
- 21.45
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Effective site finding using link anchor information. https://app.cochat.ai/papers/10.1145/383952.383999
- The paper
- Craswell, N., Hawking, D., & Robertson, S. (2001). Effective site finding using link anchor information. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/383952.383999
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.