- From: Chris Bizer <chris@bizer.de>
- Date: Wed, 18 Apr 2012 00:11:47 +0200
- To: "'Martin Hepp'" <martin.hepp@unibw.de>, <public-vocabs@w3.org>, <public-lod@w3.org>
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 http://lists.w3.org/Archives/Public/public-vocabs/2012Mar/0093.html 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. Cheers, Chris -----Ursprüngliche Nachricht----- Von: Martin Hepp [mailto:martin.hepp@unibw.de] Gesendet: Dienstag, 17. April 2012 15:26 An: public-vocabs@w3.org Vocabularies; public-lod@w3.org; Chris Bizer Betreff: Re: ANN: WebDataCommons.org - 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 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 phone: +49-(0)89-6004-4217 fax: +49-(0)89-6004-4620 www: http://www.unibw.de/ebusiness/ (group) http://www.heppnetz.de/ (personal) skype: mfhepp twitter: mfhepp Check out GoodRelations for E-Commerce on the Web of Linked Data! ================================================================= * Project Main Page: http://purl.org/goodrelations/
Received on Tuesday, 17 April 2012 22:12:20 UTC