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.h...