This item is a Poster.
Given only the URL of a web page, can we identify its topic? This is the question that we examine in this paper. Usually, web pages are classiﬁed using their content , but a URL-only classiﬁer is preferable, (i) when speed is crucial, (ii) to enable content ﬁltering before an (objectionable) web page is downloaded, (iii) when a page’s content is hidden in images, (iv) to annotate hyperlinks in a personalized web browser, without fetching the target page, and (v) when a focused crawler wants to infer the topic of a target page before devoting bandwidth to download it. We apply a machine learning approach to the topic identiﬁcation task and evaluate its performance in extensive experiments on categorized web pages from the Open Directory Project (ODP). When training separate binary classiﬁers for each topic, we achieve typical F-measure values between 80 and 85, and a typical precision of around 85. We also ran experiments on a small data set of university web pages. For the task of classifying these pages into faculty, student, course and project pages, our methods improve over previous approaches by 13.8 points of F-measure.
Fun web stuff for this record
- RKBExplorer (from linked data workshop)
- URI: http://eprints.rkbexplorer.com/id/www2009/eprints-135
Browse the data for this paper at RKBExplorer
- REST Interface
- ORE Resource Map
- ORE was described in the Linked Data Workshop. View Resource Map
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About this site
This website has been set up for WWW2009 by Christopher Gutteridge of the University of Southampton, using our EPrints software.
Add your Slides, Posters, Supporting data, whatnots...
If you are presenting a paper or poster and have slides or supporting material you would like to have permentently made public at this website, please email
email@example.com - Include the file(s), a note to say if they are presentations, supporting material or whatnot, and the URL of the paper/poster from this site. eg. http://www2009.eprints.org/128/
It's impractical to add all the workshops at WWW2009 by hand, but if you can provide me with the metadata in a machine readable way, I'll have a go at importing it. If you are good at slinging XML, my ideal import format is visible at http://www2009.eprints.org/import_example.xml
We (Southampton EPrints Project) intend to preserve the files and HTML pages of this site for many years, however we will turn it into flat files for long term preservation. This means that at some point in the months after the conference the search, metadata-export, JSON interface, OAI etc. will be disabled as we "fossilize" the site. Please plan accordingly. Feel free to ask nicely for us to keep the dynamic site online longer if there's a rally good (or cool) use for it...
- WWW2009 EPrints supports OAI 2.0 with a base URL of http://www2009.eprints.org/cgi/oai2
- The JSON URL is http://www2009.eprints.org/cgi/json?callback=function&eprintid=number
To prevent google killing the server by hammering these tools, the /cgi/ URL's are denied to robots.txt - ask Chris if you want an exception made.
Feel free to contact me (Christopher Gutteridge) with any other queries or suggestions. ...Or if you do something cool with the data which we should link to!
These are not directly related to the EPrints set up, but may be of use to delegates.
- Social tool links
- I've put links in the page header to the WWW2009 stuff on flickr, facebook and to a page which will let you watch the #www2009 tag on Twitter. Not really the right place, but not yet made it onto the main conference homepage. Send me any suggestions for new links.