Similar to abstracting maps and geography into GIS data and getting things like geographic proximity and POI-type filtering with lower overhead than creating a category tree for place articles in Wikipedia.

For instance, Wikipedia right now relies quite a lot entirely on manual tagging (authored categories) for classifying related subjects. If you want a list of all notable association footballers, for instance, then the best way to get one is to go to Category:Association football players. But then you're stuck in a very human, flawed, and often in-flux attempt to reach a consensus definition of that, and the list remains out of reach. (Hell, American players are categorized as "soccer players" under the same tree, confounding things like search, because that's the kind of thing Americans do.)

With abstraction, you get classification for much less, and the consensus problem moves from an arbitrary, authored category tree to a much narrower space. If an article is about a footballer, and the abstract data for that subject contains occupation Q937857 (association football player). The dialect and language don't matter — a footballer is a footballer. If you just want a list of footballers, you can get just a list of footballers without even going near things like SPARQL: https://www.wikidata.org/w/index.php?title=Special:WhatLinks...