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

Hi Martin,

we clearly say on the WebDataCommons website as well as in the announcement
that we are extracting data from 1.4 billion web pages only. 

The Web is obviously much larger. Thus it is also obvious that we don't have
all data in our dataset.

See for
the original announcement.

Quote from the announcement:

"We hope that Web Data Commons will be useful to the community by:

+ easing the access to Mircodata, Mircoformat and RDFa data, as you do not
need to crawl the Web yourself anymore in order to get access to a fair
portion of the structured data that is currently available on the Web.

+ laying the foundation for the more detailed analysis of the deployment of
the different technologies.

+ providing seed URLs for focused Web crawls that dig deeper into the
websites that offer a specific type of data."

Please notice the words "fair portion", "more detailed analysis" and "seed
URLs for focused Web crawls".

I agree with you that a crawler that would especially look for data would
use a different crawling strategy.

The source code of the CommonCrawl crawler as well as the WebDataCommons
extraction code is available online under open licenses.

Thus if you don't like the CommonCrawl crawling strategy, you are highly
invited to change the ranking algorithm in any way you like, dig deeper into
the websites that we identified and publish the resulting data. 

This would be a really useful service to the community in addition to
criticizing other people's work.



-----Ursprüngliche Nachricht-----
Von: Martin Hepp [] 
Gesendet: Dienstag, 17. April 2012 15:26
An: Vocabularies;; Chris Bizer
Betreff: Re: ANN: - Offering 3.2 billion quads current
RDFa, Microdata and Miroformat data extracted from 65.4 million websites

Dear Chris, all,

while reading the paper [1] I think I found a possible explanation why 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.


1. Main page: 
--> Pagerank 5 of 10

2. Category page:
--> Pagerank 3 of 10

3. Item page:
--> 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

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

Best wishes


Note: For estimating the Pagerank in this example, I used the online-service
[2], which may provide only an approximation.



martin hepp
e-business & web science research group
universitaet der bundeswehr muenchen

phone:   +49-(0)89-6004-4217
fax:     +49-(0)89-6004-4620
www: (group) (personal)
skype:   mfhepp 
twitter: mfhepp

Check out GoodRelations for E-Commerce on the Web of Linked Data!
* Project Main Page:

Received on Tuesday, 17 April 2012 22:12:20 UTC