I was reading Ullman’s Foundations of Computer Science on abstraction when it occurred to me:
A topic map legend is an abstraction that bridges some set of abstractions (read data models), to enable us to navigate and possibly combine data from them.
with the corollary:
Any topic map legend is itself an abstraction that is subject to being bridged for navigation or data combination purposes.
The first statement recognizes that there are no Ur abstractions that will dispel all others. Never have been, never will be.
If the history of CS teaches anything, it is the ephemeral nature of modeling.
The latest hot item is JSON but it was COBOL some, well, more years ago than I care to say. Nothing against JSON but in five years or less, it will either be fairly common or footnoted in dissertations.
The important thing is that we will have data stored in JSON for a very long time. Whether it gains in popularity or no.
We could say everyone will convert to the XXX format of years hence, but in fact that never happens.
Legacy systems (some at defense facilities, systems still require punched data entry, simply not economical to re-write/debug a new system) need the data, cost of the conversion, cost of verification, etc.
The corollary recognizes that once written, a topic map of a set of data models, the topic map itself becomes a data model for navigation/aggregation.
Otherwise we fall into the same trap as the data model paradigms that posit they will be the data model that dispels all others.
There are no cases where that has happened, either in digital times or in the millennia of data models that preceded digital times.
The emphasis on subject identify in topic maps facilitates the bridging of data models and having a useful result when we do.
What data models would you like to bridge today?