Backing up a bit myself, I've been trying to establish two related, but distinct points:

- There is not a single unambiguous or consistent hierarchical structure to topical knowledge overall. This is a trap numerous organisational attempts, dating to Aristotle, have fallen into. (The history and attempts themselves are fascinating, for those into that sort of thing.) That is, the hierarchy as a whole is not directed.

- An attempt at such a mapping is frequently made where a work or works (frequently: encyclopedias, in the former case, libraries in the latter) must be arranged such that there is a single canonical ordinal or spatial organisation. There are other alternatives, such as alphabetic ordering used in many dictionaries and encyclopedias. This works where the alphabet involved is reasonably compact, has a definitive single collation order, and entries themselves are at least reasonably distributed across the indexing scheme (e.g., there's not a single character with an overwhelming number of topics). It's effectively a hash using the first character under the writing system.

- Even in the far more relaxed case of a defined relationship (RDF triples are frequently used in bibliographic classification), there are cases of relations which are not themselves directed. E.g., authorship is a relationship which exists between one or more persons and a work. A work has an author, an author has works. Similarly, two people are "in" a relationship. Remove authorship or relationship, and all the relations are removed. (Though there may be unrequited love, unrequited authorship is less often observed.) Though we might impute a relationship based on some other: if work has author, then the relationship author -> work can be imputed. This despite the fact that it is the author who volitionally created the work in the first place.... It's ... confusing.

Tagging is a form of RDF triple. Entity has tag. That gives us object and object, but not much by way of predicate, other than "has".

Your Ultra Recall example doesn't do much for me as I'm not familiar with it and can find little information online other than a CNET download link. That said, I'm vaguely familiar with knowledge management systems. UR seems vaguely similar to Hypercard or a Wiki, though with the significant distinction in your description that UR apparently has an explicit parent-child relationship between nodes, whereas in a Wiki (and AFAIU Hypercard), relationships are merely as peers. As you note, this ... can lead to awkwardness and ambiguity, as well as imposing a cognitive load when creating a new node.

OTOH, there are entities which have a specified order. The collection of characters within this post would have much less utility as an unordered (or differently-ordered) set. Pages and chapters in a book, volumes or episodes in a series, scenes in a film, slides in a presentation ... without a specific order, much value is lost.

The alternative I favour is for some metadata to be inherent or intrinsic, whilst other metadata is specifically assigned, created, or evolves.

Intrinsic metadata might include: contents, creation (or aquisition/curation) date, author(s) (if known, whcih is generally the case for works created within a given management system). In general, time may well be the most universal initial placement mechanism, that is, thinking of the system as a filesystem, date-based access is a principle access method.

Pretty much all other metadata is either explicitly assigned or occurs through interactions. A work might be linked from another. It could have a specific title and subject(s) assigned. Translations, summaries, and reviews might be created from it. It might be cited, or cite other works. That's all metadata, but it isn't endogenously entrinsic to the work itself. (Citations of other works approach this, but still need to be distinguished from a fully endogenous characteristic as a citation references something external.)

An area I've been exploring but haven't really settled on or found a satisfactory standard is development of word-ngrams or tuples from within the document itself. Whitespace-delimited word clusters, ranging from 2--5 typically, can be highly useful in identifying common, distinctive, and unique elements within works. There are numerous issues with flattening the space (capitalisation, stemming, spelling variants, transliterations, homophones, homoglyphs, character substitutions), but these are a powerful tool. Ngrams are compute and space intensive, but seem quite useful. (For a time Amazon offered a "statistically distinctive phrases" or similar feature for books, showing terms that occurred with far greater than typical frequency within a given work. That's been retired for years.)

Another space I've been exploring has been various topical classification schemes, including both the Library of Congress Classification (the A-Z subject headings and subdivisions) and the Library of Congress Subject Headings (an independent set of descriptions frequently found in the LoC classification section of a book's publication and copyright page(s). Trying to find a single, or a sufficient-but-useful-and-not-excessive set of high-level summaries for works is its own interesting task. My sense is that much of that might be offloaded to an AI based on a known corpus (the LoC have catalogued some 40--50 million works, there are additional samples through published academic articles approaching 100m works, this might prove viable, given full-text access).

And, depending on the types of works you're interested in classifying, there's the question of how to approach images, audio, musical composition, choreography, video, data, and software. Perhaps also plastic arts (sculpture or physical objects), etc., etc. There's also the question of how to distinguish between levels or degrees of a specific work --- in bibliographic literature, the Work / Manifestation / Instance distinction. War and Peace by Leo Tolstoy is a work, the English translation is a manifestation, and the 1972 BBC teleplay on videocassette would be an instance, for example.

Casting a wider net, there are transactions (financial and others), and the plethora of data presently being captured by various surveillance apparatuses. The question of whether improving cataloguing of this would even be of positive value occurs ... there's nothing quite so dangerous as a highly-structured data trove. I've been trying desperately to get into Paul Otlet's work and subsequent efforts to look at approaches tried, and their successes or failures.

I'm leaning toward a system where metadata is aquired at a set of levels:

- Intrinsic: creation/aquisition date, contents, format.

- General metadata: title, author, publisher, translator.

- Curated metadata: topics, citations, references, projects and workflow.

Back to the top of this thread though: I don't see these as specifically hierarchical or directed, and from what I've seen, things get muddled where such requirements are imposed where they do not have to be. Again, your Ultra Recall example seems to illustrate this.

Apologies for length, the topic is of interest.

#kfc #webfs #docfs