Factual, which is mining the web for knowledge using a variety of web mining methods, has released an API in the local space which aims to expose, for a specific local entity (e.g. a restaurant) the places on the web that it is mentioned. For example, you might find for a restaurant its homepage, its listing on Yelp, its listing on UrbanSpoon, etc.
This mapping between entities and mentions is potentially a powerful utility. Given all these mentions, if some of the data changes (e.g. via a user update on a Yelp page) then the central knowledge base information for that entity can be updated.
You can try out the system at this demo site.
There are many challenges in this enterprise. Perhaps most importantly, there is the central issue of matching records. While there are obvious scale and performance concerns (do you really want to match 10 million Yelp entities against 10 million Yelp entities?) the key problem is, when comparing two records determining not if they are identical statements but if they are statements about the same real world entity. This is a challenge because the 'truth' about that entity may change - phone numbers change, addresses change and even names change. This can be particularly troubling with high density chain brands like Starbucks.
Browsing through the data on Factual's demo site, I came across many cases where this matching was overly aggresive (what we call over matches - statements about multiple real world entities being conflated onto a single entity).
For example, this entity:
Wild Ginger Thai Cuisine
Norwell, MA 02061 US
Is linked to the following:
but also has links to
370 Columbia RdHanover, MA 02339
The Make & Take Kitchen
Maung Thai Restaurant
74 North StHingham, MA 02043
57 Washington St, Norwell, MA 02061
Wild Ginger China Bistro
3694 Burbank Rd
1831 Beall Ave, Wooster, OH 44691
The fundamental problem here is that a number of these sites, while they have a 'main' entity on their page, also mention other entities (e.g. for recommending alternatives to the user). However, these recommendations are not guaranteed to be stable (you may get different recommendations at any time). This fundamental problem illustrates why document analysis (understanding the layout of the document and consequently what the document is 'about' through the position of mentions of entities) is key if one wants to accurately mine the web for knowledge of this sort.