Len, (01)
LY> I found this presentation which compares several practical
> approaches using experimental data. It also provides a (predicted)
> time line for future progress.
>
http://www.larkc.eu/wp-content/uploads/2008/04/larkc-kickoff-meeting-greaves.pdf (02)
That is an interesting presentation. But it never defines the term
LarKC or even explains the acronym -- that's because it was presented
at a meeting where people already knew the term. (03)
For the record, LarKC is supposed to be a Large Knowledge Collider.
Following is a FAQ sheet: (04)
http://wiki.larkc.eu/LarkcProject/FAQ (05)
I recommend the following 6-page overview: (06)
http://www.larkc.eu/wp-content/uploads/2008/05/larkc-icsc08.pdf (07)
Following is a 22 megabyte ppt "vision" with lots of gee-whiz graphics,
which I wouldn't recommend unless you have a high-speed line: (08)
http://www.larkc.eu/wp-content/uploads/larkc-in-a-nutshell.ppt (09)
A short summary of my views on the project: (010)
1. I agree with many of the points that Mark Greaves makes in the
first presentation above. (011)
2. I strongly endorse one of the goals of the LarKC project:
"Enriching the current logic-based Semantic Web reasoning
methods with methods from information retrieval, machine
learning, information theory, databases, and probabilistic
reasoning." (012)
3. I also endorse their goal of removing "the scalability
barriers of currently existing reasoning systems." (013)
3. But I have serious doubts about a prediction by Gartner that
by 2012, 70% of the web pages will be marked up with RDF. (014)
4. And I am highly skeptical about projects that bring together
14 different groups, each blessed with half a million euros,
with the expectation that they will magically produce a
major new breakthrough. (015)
Although I believe that XML tags (including RDF) are valuable for
many purposes, I believe that we can extract much more semantic
information from raw, untagged natural language texts. (016)
At VivoMind, we have been doing that. And we have achieved goals
#2 and #3 above without using tagged texts. We have processed
gigabytes and terabytes of NL text with an ordinary 8-CPU server. (017)
John (018)
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