> I, personally, personally cannot see this as a problem that can be solved. Maybe you are better.

Both library science and cognitive linguistics have solutions for/discuss this problem.

I would say what they call 'composition by capability' is a theory of categorization, in fact similar to Wittgenstein's family resemblance theory.

That said, it's not obvious that the underlying data model needs or should map to a theoretical model for how human cognition works. Maybe it should and it's worth considering the theoretical landscape before setting out, but there are other features that come into play.

I know very little about library science, but I assume they take a more practical (for this task) approach to this that's worth looking into.

You should definitely look into it, especially if you like pedantic arguments.

This runs the very real risk of thinking you can overload all "tags" onto something that you want.

That is, embrace the restrictions that come from a taxonomy. It is a bit of a lie, but a lot of it is defined on contrasts and these contrasts help build things.

What distinguishes a car from a truck? One is more faceted to holding people than to holding cargo. But if you don't know either a truck or a car, you are unlikely to know from just that description. Indeed, you could wind up with a cargo van. Which is different from a passenger van, in much the same way.

To that end, we present the taxonomy as a heirarchy of descriptive properties. When, in many cases, it is a heirarchy of representative samples. In programming, we distinguish between class based and prototype based object oriented representation. In reality, most taxonomies are presenting class style based relationships, but using prototype based representation from a population.

Not disagreeing but unfortunately a lot of data sources have different opinions on this. And sometimes categorizations are actually useful indicators to tell what is what.

Composable and namespaced tags is exactly what we do in my company. It gives us a lot of flexibility and it mirrors what OSM does. You can do a lot of duck typing against such a system and it will work as good as your data is without breaking completely when you get bad data. You just loose some features.

> At what point is the car a truck?

when you can use it as a truck (ie. when it can do what the car isn't adequate for).

Discrete categorization of continuous phenomena is always going to be inherently arbitrary. It's important to keep in mind what your goals are. This sort of categorization shouldn't be done with the goal of finding some sort of fundamental platonic truth; that's not going to work. Rather, discrete categorization is performed simply because humans find these categories useful when communicating.

The fuzzy line between truck and car isn't a problem when you approach categorization with this mindset. If you see a bank robber fleeing in an El Camino, you can tell the cops they fled in a "truckish car" or a "carish truck"; you don't have to neatly categorize an El Camino as one or the other to get your point across, but the arbitrary categories still help you communicate the idea.

If you're tagging pictures and come across an El Camino, and you don't have a "coupe utility vehicle" tag, you can simply tag the El Camino as both a truck and a car.