Shilad, Edgard,

replying here to this and related threads.

1) Analytics Engineering (primarily Christian Aistleitner and Erik Zachte) is currently working on defining how pageviews are counted and extracted from the raw request logs. This work is part of a larger effort to replace the legacy webstatscollector [1], which will produce more accurate data as well as the ability to parse requests in a more flexible way. Christian and Erik should be able to comment on whether referral and search query string extraction for inbound traffic are use cases falling within the initial scope of this project. The publication of this data is a different matter (see below).

2) Releasing anonymized internal search data is not AFAIK one of the priorities the team is currently working on. As Andrew noted in a previous thread, engineering effort aside, further releases of private data will be subject to the new privacy policy which is currently being discussed on Meta [2]. I don’t expect we’ll invest any effort into anonymizing or aggregating data for the purpose of publication until the privacy policy consultation is settled. Search is also undergoing a major overhaul [3]

3) As per Federico, a short description of all logs generated at Wikimedia (including MediaWiki logs, page request logs, search logs and EventLogging data) can be found at on Wikitech [4]

Dario

[1] https://wikitech.wikimedia.org/wiki/Analytics/Webstatscollector

On Dec 10, 2013, at 10:09 PM, Shilad Sen <ssen@macalester.edu> wrote:

Greetings!

I'm a Professor at Macalester College in Minnesota, and I have been collaborating with Brent Hecht and many students to develop a Java framework for extracting multilingual knowledge from Wikipedia [1]. The framework is pre-alpha now, but we hope to offer a stable release in the next month.

Given a phrase (e.g. "apple"), our library must identifying articles associated with a phrase. This is a probabilistic question. How likely is the phrase "apple" to refer to the article about the fruit vs the company? This simple task (often called Wikification or disambiguation) forms the basis of many NLP algorithms.

Google and Stanford have released an awesome dataset to support this task [2]. It contains the *text* of all internet hyperlinks to Wikipedia articles. This dataset makes the problem much easier, but it has two serious deficiencies. First, it only contains links to articles in English Wikipedia. Second, it was generated once by Google, and it is unlikely Google will update it.

The WMF could create a similar dataset by publishing the most common inbound search queries for all WP pages across all language editions. This dataset would enable individuals, researchers and small companies (not just Google and Microsoft) to harness Wikipedia data for their applications.

Does this seem remotely possible? I've thought a little about engineering and privacy issues related to the dataset. Neither are trivial, but I think they are feasible, and I'd be happy to volunteer my engineering effort.

If you think the idea has legs, how do we develop a more formal proposal about the dataset?

Thanks for your feedback!

-Shilad

[1] https://github.com/shilad/wikAPIdia
[2] http://googleresearch.blogspot.com/2012/05/from-words-to-concepts-and-back.html

--
Shilad W. Sen
Assistant Professor
Mathematics, Statistics, and Computer Science Dept.
Macalester College
ssen@macalester.edu
651-696-6273
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