How weird. This thread is suddenly all about conceptual modeling:
Here’s food for thought. The conventional approach to conceptual modeling is the main paradigm being discussed here: classification. When we set up any information system, we tend to begin by answering “What kinds of data are we putting in this system?” Implicit in this question is the idea of “kinds”: People, Locations, Projects, Events, as @ja_rule mentions, are an example of a “kind.” But so are “Notes” or “Blocks” as has been discussed elsewhere here. The idea of assuming that everything has a kind is so embedded in our thinking that we rarely question it.
Before I go on, let me clarify something real quick. In information modelling, we typically refer to these types of kinds as classes. An entry in a database is an instance. In other words, the real-world stuff we put into information systems are instances of stuff, and we conventionally instantiate our stuff by saying it is one of a given class of thing.
In the conventional paradigm, classes (and the relationships between them) make up the system’s conceptual model. (The model is a digital representation of the ontology of the domain, from our perspective.)
However, another option exists. After all, why do things need to be a kind before they are a thing?
As the “You are a person” example illustrates, every instance is actually unique. By classifying everything, all the time, we may actually degrade the quality of information in that instance. As @nickmilo’s example shows, when we classify a person as “born in the 1980s,” we tend to lose the data that they were “born in Canada.” (That is a strikingly bad conceptual model, but hopefully it illustrates the point.)
As it happens, by developing these ideas in this thread, y’all have essentially reproduced my PhD supervisor’s early papers (e.g., “Emancipating instances from the tyranny of classes in information modeling”).
This—all of this!—is why flatter structures tend to be better for information quality. This is especially true when the purpose of the information isn’t known from the start, or when the information may be used in ways originally unintended.
In order to resist the paradigm of classify-first information-later, we can embed as much richness in our instances as possible. In other words, when capturing information, go for richness, and include rich semantics and metadata so that you’re representing the thing you’re capturing as completely and usefully as possible.
In other other words: when you’re note-taking, capture rich notes, use useful tags, and keep good metadata (e.g., always list a book’s title in the same form along with a quote from the book).
Obviously, though, just because flat structures are good for information quality doesn’t mean they’re good for information use. Classification has many cognitive benefits. Economy is one: it is easier to think of a person by representing only their basic demographics than by empathizing with their person-hood—especially at scale. Inference is another: when we see a piece of data and know that it’s a Person, we can infer that it has a birth date and some kind of citizenship.
So what? Well, if we’ve captured rich data in a flat structure, we can then layer purpose-built conceptual models on top of that data.
In other words, with a flat data structure, we can use tools like smart searches, tag panels, and ontology notes (what Nick calls “maps of content” 
) to view, filter, query, and organize rich data according to whatever need we might have in a given moment.
None of will be new or surprising to most, I suspect, but I hope it’s interesting to note that there’s science behind the conclusions that have been discussed already!
In terms of this thread, I think the key practices people should adopt are:
- Capture notes with richness and good metadata.
- Flatten your data structure as much as possible.
- Create purposeful relationships between notes. I.e., don’t create conceptual models—that is, build organizing structure, or relate notes to one another—based on some predicted, anticipated need. Only organize your notes according to an actual, current need.
- Such a need, of course, could simply be to explore an interest of yours. The “need” to put your newest note somewhere doesn’t count.
- When you do have a purpose, choose how to organize (and what tools you should use) based on that purpose.
- If exploring an interest is the purpose, a Map of Content/Ontology note is probably be best approach to organizing your notes.
- If managing inline tasks while you research is the purpose, creating a backlink collection with a pseudo-tag (as explained here by @deftdeg) is probably the best organizing approach. Hashtags could also suit.
- If you’re putting together a project or a publication, a speculative outline is probably the best organizing approach.
I hope this landed! If it did, a question for anyone interested: what other purpose → organizing approach pairs are out there?
…apparently I’ve been paying attention in my PhD