Hating the Semantic Web is like shooting fish in a barrel ever since Clay Shirky wrote an essay that lays bare the goals as being irrelevant and immaterial.
http://www.shirky.com/writings/herecomeseverybody/semantic_s...
http://www.shirky.com/writings/herecomeseverybody/semantic_s...
US citizens are people
The First Amendment covers the rights of US citizens
Nike is protected by the First Amendment
I'm sure this guy is very clever, and has plenty of great qualities - but he has built his argument around the failure of logic, when it is clear that he doesn't know very much about it. The use of very poorly defined terms makes it look like he is aware of this...
The problem with author's thesis is that he want's semantic web to universally describe the whole world. This is impossible because the world is changing and sometimes contradictory.
OWL for instance doesn't cover such assumptions, and in general most knowledge representation frameworks are monotonic, so it's impossible for instance to withdraw some fact by adding a new one. That what might be a real limitation of the Semantic Web as we know it.
> the world is changing and sometimes contradictory
If you think ontologies don't change and will not require constant maintenance I have a bridge to sell you.
F=m*a is a great rule most of the time that happens to stop working at various edge cases. Sadly, you can say the same of all of physics and just about every other rule. Even things as 'simple' as living vs dead can get really complicated with lots of odd edge cases.
http://en.m.wikipedia.org/wiki/HeLa new species?
Well, automated systems constructed by people - as ontology construction and semantic reasoner writing is primarily a human activity (... yeah I see it). It would be pretty funny if we end up with a semantic web, where logic is king, having ontologies mapped by a probabilistic AI though.
As far as edge cases are concerned, that is why you use an open world assumption [0], allowing the reasoner to return "Unknown". Which would act as a compiler error for human understating. Of course that won't save you from reclassifications, but nothing will. I haven't done much thinking on the matter, but I can't think of anything that would better handle the expansion of a knowledge graph than an OWA semantic reasoner. I've done a bit of work with neural networks, and changes in the domain are not well handled by neural networks. I imagine the results of a large graph change would create in the neural network something that would look very human - confirmation bias :)
If you've set things up with that sort of constraint, then you've set yourself up for failure - Gödel's incompleteness theorems [0]. I think OWA is the only way around that, you just need to gracefully handle unknowns. Of course, you can convert an unknown into a known pretty easily with: ¬u :)
> For instance in the case of cardinality problem.
That is exactly when I'd want my reasoner to ignore a graph edge, or else you get a non sequitur that ends with Nike belonging in the human set. Measure twice, cut once.
[0] http://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_t...
Also, he mentions Google in this essay; this was written after the emergence of Google, when Google was trouncing all of these other services that tried to use metadata embedded in webpages or tried to classify all websites into some big hierarchy.
A world of exhaustive, reliable metadata would be a utopia. It's also a pipe-dream, founded on self-delusion, nerd hubris and hysterically inflated market opportunities.
The comment at the end of the essay about Google looks to be half throw-away. It's like he showed the essay to someone before he published and they said, "but Google", so he put a comment about Google in there without acknowledging that they were wrecking his essay.
Additionally, he never mentions the positive feedback loop created by companies like Google that tend to encourage good meta data creation by punishing the obvious crap.