The Wikipedia article on unstructured data makes it clear that data may have a structure, but that “unstructured data” means one not readily recognizable to a computer.
The term unstructured data bothers me because any text has a structure. If it didn’t, we would not be able to read it. It would just be a jumble of symbols. Oh, sorry. Apologies to any AI agents “reading” this post. But that is how traditional computers see a text, just a jumble of symbols.
When people view a text, they see structure, recognize subjects, etc. Moreover, different people can look at the same text and see different structures and/or subjects.
There are topic maps that are written to enforce a “correct” view of a body of data and those are certainly useful in many cases. Topic maps also support users identifying the structures and subjects they see in a text, along side identifications made by others.
The extent to which users view texts and leave trails as it were of the structures and subjects they identified in a text (or body of texts), those trails form maps that can be useful to others.
Think of it as tagging but with explicit subject identity. The relationships to a particular text, its author, and a variety of other details could be extracted automatically and with a minimum of effort on the part of the user. A topic map application could even suggest subjects or associations for a user to confirm based on their reading.
Suggest: unmapped data.
Captures both the sense of exploration as well as allowing for multiple mappings.
Thoughts?