Visualization tool used to see if ai-one's biologically inspired computing can discern meaningful associations in the mess of tweets from a technical conference. This capability serves as the foundation for building intelligent agents and other applications allowing human interpretation of large data sets.
1. Outline of discussion
Topic-Mapper: ai-one for Text
Twitter Use Case
Semtech Data -
Title
Schema.org example
Tom Marsh February 2013
ai-one™
biologically inspired intelligence
2. Can ai-one
make sense out
of twitter data?
Source data is a file of
tweets #semtech from
days during the
conference-
Total file is approx 7800
lines of tweets, original
data shown at right.
Messy original and clean
data are used in this case
“cleaning” included
removing time and url
info
3. Assoc Tree for
schema.org – net
is from semtech
tweets
Circle indicates
area zoomed on
next slides
Schema.org was
mentioned over
120 times and was
a subject of
discussion at the
conference as
industry was not
pleased at the
announcement by
Google, Microsoft
and Yahoo of their
own standard
6. This image
versus first
slide shows
increased
quality of
semantic after
importing
messy tweets
on top of clean
tweets –
Demonstrating
simple
programmatic
enhancement
without losing
original
associations
7. This image
shows quality
of semantic
after
importing
messy tweets
Displays all
relevant
terms when
zoomed in to
show word
“bitch”
9. Did the word make “sense”?
The word “bitch” was used in only one
tweet and accurately paraphrased the
conversation
7:35 nopiedra <http://www.twitter.com/nopiedra> #linkeddata RT
@paul_houle <http://www.twitter.com/paul_houle>: was a bit
depressed that people at #semtech would rather bitch about
schema.org than swap war stories
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