The DOI matches its Crossref record.
Using neighbors to date web documents
Abstract
Time has been successfully used as a feature in web information retrieval tasks. In this context, estimating a document's inception date or last update date is a necessary task. Classic approaches have used HTTP header fields to estimate a document's last update time. The main problem with this approach is that it is applicable to a small part of web documents. In this work, we evaluate an alternative strategy based on a document's neighborhood. Using a random sample containing 10,000 URLs from the Yahoo! Directory, we study each document's links and media assets to determine its age. If we only consider isolated documents, we are able to date 52% of them. Including the document's neighborhood, we are able to estimate the date of more than 86% of the same sample. Also, we find that estimates differ significantly according to the type of neighbors used. The most reliable estimates are based on the document's media assets, while the worst estimates are based on incoming links. These results are experimentally evaluated with a real world application using different datasets.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“One reason for this, as observed by [Nunes et al. 2007], is due to the fact that web servers typically do not provide other temporal information than the crawling date.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Nunes et al. 2007
In the citing paper
“Determining a document's timestamp was first studied by [Toyoda and Kitsuregawa 2006] and [Nunes et al. 2007].”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Nunes et al. 2007
In the citing paper
“[Nunes et al. 2007] employed link structure analysis by considering three types of features derived from the web, incoming links, outgoing links, and HTML src attributes”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Nunes et al. 2007
In the citing paper
“Such an approach is limited however by two main constraints: (1) the need to have a huge set of external documents and (2) the requirement that the last-modified dates exist in a set of external sources”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Nunes et al. 2007
In the citing paper
“Both used neighboring pages to estimate the document creation date or the last-modified date”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Nunes et al. 2007
Citations
Cites
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Cited by
Sorted by citations
Survey of Temporal Information Retrieval and Related Applications
Campos, R., Dias, G., Jorge, A. M., & Jatowt, A. (2015) · ACM Computing Surveys · doi:10.1145/2619088 · Cited by 262
Metadata
- Authors
- Nunes, S., Ribeiro, C., & David, G.
- Affiliations
- Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
- Faculdade de Engenharia da Universidade do Porto/INESC-Porto, Porto, Portugal
- Venue
- Proceedings of the 9th annual ACM international workshop on Web information and data management
- Publisher
- ACM
- Published
- 2007-11-09
- Pages
- 129-136
- Cited by
- 17
- References
- 0
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.
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