Re: ANN: WebDataCommons.org - Offering 3.2 billion quads current RDFa, Microdata and Miroformat data extracted from 65.4 million websites

Hi Martin,

By incorporating PageRank into the decision of what pages to crawl, 
CommonCrawl is actually trying to approximate what search engine 
crawlers are doing. In general, search engines would collect pages that 
would be more likely to rank higher in search results, and PageRank is 
an important component of that.

PageRank does transfer along the edges of the web graph, so a highly 
ranked homepage would transfer it's PageRank to the pages leading from it.

My only complaints about CommonCrawl in this regard is that they don't 
publish their webgraph and the computed scores. It's a valuable resource 
to have. Further, they should compute it regularly... it seems they have 
two dumps with two years apart, and if they used the PageRank scores 
from the first dump to crawl the second, that might be a bit off.

Cheers,
Peter



On 4/17/12 3:25 PM, Martin Hepp wrote:
> Dear Chris, all,
>
> while reading the paper [1] I think I found a possible explanation why WebDataCommons.org does not fulfill the high expectations regarding the completeness and coverage.
>
> It seems that CommonCrawl filters pages by Pagerank in order to determine the feasible subset of URIs for the crawl. While this may be okay for a generic Web crawl, for linguistics purposes, or for training machine-learning components, it is a dead end if you want to extract structured data, since the interesting markup typically resides in the *deep links* of dynamic Web applications, e.g. the product item pages in shops, the individual event pages in ticket systems, etc.
>
> Those pages often have a very low Pagerank, even when they are part of very prestigious Web sites with a high Pagerank for the main landing page.
>
> Example:
>
> 1. Main page: 	http://www.wayfair.com/
> -->  Pagerank 5 of 10
>
> 2. Category page:	http://www.wayfair.com/Lighting-C77859.html
> -->  Pagerank 3 of 10
>
> 3. Item page:	http://www.wayfair.com/Golden-Lighting-Cerchi-Flush-Mount-in-Chrome-1030-FM-CH-GNL1849.html
> -->  Pagerank of 0 / 10
>
> Now, the RDFa on this site is in the 2 Million item pages only. Filtering out the deep link in the original crawl means you are removing the HTML that contains the actual data.
>
> In your paper [1], you kind of downplay that limitation by saying that this approach yielded "snapshots of the popular part of the web.". I think "popular" is very misleading in here because the Pagerank does not work very well for the "deep" Web, because those pages are typically lacking external links almost completely, and due to their huge number per site, they earn only a minimal Pagerank from their main site, which provides the link or links.
>
> So, once again, I think your approach is NOT suitable for yielding a corpus of usable data at Web scale, and the statistics you derive are likely very much skewed, because you look only at landing pages and popular overview pages of sites, while the real data is in HTML pages not contained in the basic crawl.
>
> Please interprete your findings in the light of these limitations. I am saying this so strongly because I already saw many tweets cherishing the paper as "now we have the definitive statistics on structured data on the Web".
>
>
> Best wishes
>
> Martin
>
> Note: For estimating the Pagerank in this example, I used the online-service [2], which may provide only an approximation.
>
>
> [1] http://events.linkeddata.org/ldow2012/papers/ldow2012-inv-paper-2.pdf
>
> [2] http://www.prchecker.info/check_page_rank.php
>
> --------------------------------------------------------
> martin hepp
> e-business&  web science research group
> universitaet der bundeswehr muenchen
>
> e-mail:  hepp@ebusiness-unibw.org
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> fax:     +49-(0)89-6004-4620
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>           http://www.heppnetz.de/ (personal)
> skype:   mfhepp
> twitter: mfhepp
>
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> =================================================================
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>
>

Received on Tuesday, 17 April 2012 14:50:37 UTC