

Clay Shirky: Where the Semantic Web gets it wrong - hendler
http://www.shirky.com/writings/semantic_syllogism.html

======
pius
An interesting, but frustrating read. Shirky makes statements that conflate
what requires thought with what's hard and what's hard with what's impossible.

For example, he says:

 __*

Consider the following assertions:

    
    
        - Count Dracula is a Vampire
        - Count Dracula lives in Transylvania
        - Transylvania is a region of Romania
        - Vampires are not real
    

You can draw only one non-clashing conclusion from such a set of assertions --
Romania isn't real. That's wrong, of course, but the wrongness is nowhere
reflected in these statements. There is simply no way to cleanly separate fact
from fiction, and this matters in surprising and subtle ways that relate to
matters far more weighty than vampiric identity.

 __*

Note the last part: _There is simply no way to cleanly separate fact from
fiction._ Usually statements that start off as "there is simply no way" are
wrong and this one is no exception. This and many of the other objections he
raised can be addressed by using the right ontologies in combination with
techniques like fuzzy logic and webs of trust.

Also, one of his big arguments is that the Semantic Web will never work
because the data will always incomplete. He characterizes the Semantic Web as
assuming a simple world in which all information is known. This is completely
false though, as the Semantic Web standards have specifically been architected
with the Open World Assumption in mind.

<http://en.wikipedia.org/wiki/Open_World_Assumption>

That's a pretty fundamental thing to get wrong if you're going to try to talk
intelligently about these issues.

------
hendler
And my response [http://semanticsearch.org/where-clay-shirky-misses-
semantic-...](http://semanticsearch.org/where-clay-shirky-misses-semantic-web-
syllogism-and-worldview)

In summary:

[The article] misses the true tact of the current semantic web - which is
focused on three key areas:

    
    
       1. identity
       2. creating quality data (whether manually or using Natural Language processing)
       3. scaling to be a giant, online database

