Mining Wikipedia for mapping relations for text mining

Question

I am planning to develop a web-based application which could crawl wikipedia for finding relations and store it in a database. By relations, I mean searching for a name say,'Bill Gates' and find his page, download it and pull out the various information from the page and store it in a database. Information may include his date of birth, his company and a few other things. But I need to know if there is any way to find these unique data from the page, so that I could store them in a database. Any specific books or algorithms would be greatly appreciated. Also mentioning of good opensource libraries would be helpful.

Thank You

Answer 1

If you haven't already, you should have a look at DBpedia. Many categories of wiki articles have "Infoboxes" for the kinds of information you describe, and they've made a database out of it:

http://en.wikipedia.org/wiki/DBpedia

You might also leverage some of the information in Metaweb's Freebase (which overlaps and I believe may even integrate the info from DBpedia.) They have an API for querying their graph database, and there's a Python wrapper for it called freebase-python.

UPDATE: Freebase is no more; they were acquired by Google and eventually folded into the Google Knowledge Graph. There is an API but I don't think they have anything like the formal sync'ing Freebase had with public sources like Wikipedia. I'm personally disappointed in how this looks to have turned out. :-/

As for the natural language processing bit, if you do make headway on that problem you might consider these databases as repositories for any information you do mine.