Title

Outline of discussionTopic-Mapper: ai-one for Text

biologically inspired intelligence

ai-one™

Twitter Use Case

Semtech Data -

Schema.org exampleTom Marsh February 2013

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 urlinfo

Assoc Tree for schema.org – net is from semtechtweets

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

Words of Highest Relevance

closed-doorsStickingSupportiveCountlessBitchTaggingResponsesPre-ordersTrunkTapJewish HolidayLinkedDataEtc.

These words were checked in source data for relevance to schema.org

This image shows with just clean data

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

This image shows quality of semantic after importing messy tweets

Displays all relevant terms when zoomed in to show word “bitch”

This image shows increased quality of semantic after importing clean tweets on top of messy tweets

Note fewer terms of greater relevance (14 vs 18+)

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

Your brain on twitter..

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Tom Marsh, COO

© ai-one inc. 2013

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