Modeling the world is a key part of text mining. Modeling is an activity that comes after someone makes a statement like this : 'there are restaurants in the world, let's mine some web data and find out where they all are!" This is because someone else will ask "what is a restaurant?"
If we step back a little, we might think about the value of generalization in language, which is clearly linked to what we often call 'meaning'. When I say "there is a restaurant on 3rd at 5th, let's meet there" I am assuming that the things in the world that you use the word "restaurant" to describe are reasonably similar to the set that I would denote with the same term. This remarkable assumption - that we will behave in similar ways in the future in a manner determined by an act of language - is pretty much what holds the whole idea of language together.
The idea of assigning categories to things (or things to categories) can be analyzed at a number of levels.
Firstly, we might think of some natural, or objective quality of the objects in question and determine that if something has this quality, then it is in the category. This is what biologists have been doing for quite some time.
Secondly, especially in the case of various ownable things, we might consider the manner in which the owner describes it (this is a sushi bar, damn it!).
Thirdly, we might consider how people refer to the object (Japanese fast food!).
Finally, we might consider how people (or other agents) interact with the thing in a non-linguistic manner.
All of these come with various caveats. That being said, the difference between the first and all the others is the difference between objectivity and subjectivity. Someone int he UK might refer to an establishment selling a limited range of fried food for immediate consumption (but not on the premises) as a "chippie" whereas the same place in the US might be called a "fish and chip shop". These names don't necessarily change the nature of the thing in question. With the other options, we have to consider the subjective nature of the audience. This leads to a set of categories which differentiate both the space of things and the space of speakers (and even the space of contexts in which people are communicating).
The next challenge when trying to put things in buckets is specialization. We might feel that concepts like restaurant and plumber are pretty straightforward (we know what to expect when we engage with such businesses). In addition, we might have some idea of what a loung is. But what is a "Thai Restaurant and Lounge"? Is this a restaurant that is also a lounge? Or is this a restaurant that has a relationaship with another entity which is called a lounge? Is a "bar & grill" the same as a "grill & bar"? Is it a bar with a grill or is it an atomic concept in which the nature of the bar experience is altered by the intimate relationship with the grill?
The value of categories is that they help us project assumptions onto concepts about which we know almost nothing. If you tell me that your mum runs a business that is fine, but if you tell me she runs a restaurant, I will be around for some discounted food.
Ultimately, the things we can do with entities are defined by a matrix of properties. Categories are really a projection from this matrix. This is how I know how to interact with a taco truck, a kaiten sushi bar and a french restaurant the same for some things (paying for goods) and differently for others (sitting down at a table, at a bar or not at all). This matrix of fine grained facets can be used to project onto a model of convenient concepts (e.g. concept_134 for which I use the word 'restaurant' to denote, among others). There are many different terms we may use to indicate these concepts and we may do so imprecisely (it is more efficient to use a category we are all familiar even for an object that has a 2% variance in the details of some of the common facets).