I've found the rise and fall OOP to line-up well with various trends in ontologies and semantic-like KM tools (after all OOP is basically designing an ontological model you code inside of). Outside of various performance issues, the idea that you can have one master model of your problem to solve everything is an idea that hopefully seems to be running out of steam.
In the area I've worked in, I've seen numerous semantic systems come and go, all built around essentially one giant model of the slice of the world it's supposed to represent, and the projects all end up dead after a couple years because:
a) the model does a poor job representing the world
b) nobody seems to have a use-case that lines up perfectly with the model (everybody needs some slightly different version of the model to work with)
c) attempts to rectify b by just adding in more and more to the model just leaves you with an unusable messy model.
More recent systems seems to be working at a meta-model level, with fewer abstract concepts and links, rather than getting down into the weeds with such specificity, and letting people muck around in the details as they see fit. But lots of the aggregate knowledge that large-scale semantic systems are supposed to offer gets lost in this kind of approach.
I think OOP at its heart is just another case of this -- it's managed to turn a programming problem into an ontology problem. You can define great models for your software that mostly make sense, but then the promise of code-reuse means your suddenly trying to shoehorn in a model meant for a different purpose into this new project. The result is code that either tries to ignore "good" OOP practices to just get the damn job done, or over specified models that end up so complicated nobody can really work with them and introduce unbelievable organizational and software overhead.
It's not to say that OOP and other semantic modeling approach don't have merit. They're very useful tools. I think the answer might be separate models on the same problem/data, each representing a facet on the problem. But I haven't quite gotten the impression that anybody else has arrived at this in industry and are instead just going for higher levels of abstraction or dumping the models all together.
Again, OOP manages to turn programming problems into ontology problems -- which is hardly a field that's well understood, while the goal is and always has been to turn programming problems into engineering problems -- which are much more readily understood.
In the area I've worked in, I've seen numerous semantic systems come and go, all built around essentially one giant model of the slice of the world it's supposed to represent, and the projects all end up dead after a couple years because:
a) the model does a poor job representing the world
b) nobody seems to have a use-case that lines up perfectly with the model (everybody needs some slightly different version of the model to work with)
c) attempts to rectify b by just adding in more and more to the model just leaves you with an unusable messy model.
More recent systems seems to be working at a meta-model level, with fewer abstract concepts and links, rather than getting down into the weeds with such specificity, and letting people muck around in the details as they see fit. But lots of the aggregate knowledge that large-scale semantic systems are supposed to offer gets lost in this kind of approach.
I think OOP at its heart is just another case of this -- it's managed to turn a programming problem into an ontology problem. You can define great models for your software that mostly make sense, but then the promise of code-reuse means your suddenly trying to shoehorn in a model meant for a different purpose into this new project. The result is code that either tries to ignore "good" OOP practices to just get the damn job done, or over specified models that end up so complicated nobody can really work with them and introduce unbelievable organizational and software overhead.
It's not to say that OOP and other semantic modeling approach don't have merit. They're very useful tools. I think the answer might be separate models on the same problem/data, each representing a facet on the problem. But I haven't quite gotten the impression that anybody else has arrived at this in industry and are instead just going for higher levels of abstraction or dumping the models all together.
Again, OOP manages to turn programming problems into ontology problems -- which is hardly a field that's well understood, while the goal is and always has been to turn programming problems into engineering problems -- which are much more readily understood.