Kingsley, (01)
I agree with your point about LOD. But there is nothing in the
Semantic Web that helps. Watson just puts the URIs into their
UIMA notation, stores them in a relational database (DB2),
and uses Prolog for analyzing and reasoning about Jeopardy
questions. (02)
KI
> But, and this is really really important, it leveraged Linked Datasets
> such as DBpedia and others from the Linked Open Data cloud.
>
> Structured Linked Data, Machine Learning, and Natural Languages are 3
> equally critical components of the Watson story. Take one of these
> components out of the mix and Watson doesn't happen. (03)
First point: The SW does *nothing* for NLP. They ignored it totally.
They expected human annotators to insert RDF tags. That might have
been a useful quick fix in 1998. But today, a good NLP system can
derive much better information from the raw text than from the
guesses by typical annotators. (04)
The SW layer cakes emphasized Unicode, URIs, XML, and RDF as the
foundations. Google used the URIs, but stored them in JSON.
Watson also used the URIs, represented them in UIMA, and stored
them in a relational DB. (05)
RDF is just a bloated notation that anybody who needs efficiency
runs away from as fast as possible. Even the W3C replaced it
with RDFa, whose only similarity to RDF is the first 3 letters. (06)
As for DBpedia, its hierarchy of terms is abysmal. It's better
than nothing for some applications, but it really needs to be
rebuilt with some decent NLP tools. (07)
KI
> I fantasize about a Machine vs Machine edition of Jeopardy
> (in the future) that pits algorithms against algorithms where
> all parties (other players in this realm) have a common structured
> linked substrate as the knowledge base foundation (08)
I think that Watson pretty much exhausted what we can learn from
the Jeopardy game. The questions are too short and the format
depends too heavily on the speed of hitting the buzzer. (09)
Governments, businesses, and law firms have huge volumes of NL texts.
There are far more important challenges in analyzing those texts
and doing the kinds of tasks that people will pay for. (010)
For more discussion, see http://www.jfsowa.com/pubs/futures.pdf (011)
John (012)
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