02 ai-one - content analytics business cases

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biologically inspired intelligence ai-one © ai-one inc. 2012 Business cases for content analytics with ai-one

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Page 1: 02 ai-one - content analytics business cases

biologically inspired intelligence

ai-one™© ai-one inc. 2012

Business cases for content

analytics with ai-one

Page 2: 02 ai-one - content analytics business cases

Biologically Inspired Intelligence

creativitylogic

© ai-one inc. 2012

Page 3: 02 ai-one - content analytics business cases

… with only a few ai-one commands it is easy

to build and use semantics in language !

The secret of content analytics

© ai-one inc. 2012

Successionaly we show some sample

concepts how to use the ai-one approach

(commands) in daily business cases.

Page 4: 02 ai-one - content analytics business cases

Recognize the sense and meaning

within a text corpus means to identify

the semantically most important

words which build the content

Make sense of a texti.e. in: E-Mail, Article, Feed, Tweet, Story etc…

© ai-one inc. 2012

Use of the : KeyWordCommand

Page 5: 02 ai-one - content analytics business cases

Recognize the sense and meaning within a text corpus means to identify

the semantically most important words which build the content

Make sense of a texti.e. in: E-Mail, Article, Feed, Tweet, Story etc…

© ai-one inc. 2012

Use of the : KeyWordCommand

Extract the most important words in a text

corpus and form the Light Weight

Ontology (LWO) and from a digest of the

meaning. That is the condensed summary

of the sense and meaning of a text.

Now ai-one or other matcher can classify

and sort the text.

This is the ai-Fingerprint. With the for example

7 words this text is define in its sense

Page 6: 02 ai-one - content analytics business cases

Its like brainstorming, what has to-do with what, or which word is

semantically connected with which word. Find the associative and

semantic relations trough a whole big text or whole data base. Find

patterns we did not know they exist!

Find associative, semantic relationsi.e. in: E-Mail, Article, Feed, Tweet, Story etc…

© ai-one inc. 2012

Use of the : AssoAnalysCommand

WORD

WORD

WORD

WORD

WORD

WORD

WORD

WORD

WORD

WORDThis commands starts with one or multiple words

and searches for the semantically relations.

Find patterns of relations in whole text corpus.

Validate the importance of a connection

between two or multiple words

Detect association bridges between two words.

Display of semantic chains.!

Page 7: 02 ai-one - content analytics business cases

One additional challenge is the spelling. Users very often miss spell

words. Therefore we also search for syntax patterns in order to verify

the words.

© ai-one inc. 2012

Find syntax patternsi.e. E-Mail, Article, Feed, Tweet, Story etc…

Use of the : PhoneticCommand

• Maier

• Mair

• Paier

• Meier

The Phonetic pattern recognition on syntax

is very helpfully to identify similar

words, spell errors and artificially re-

designed words.

Find word pattern, where also the first

character may be wrong!

• Meyer

• Peyer

• Peier

• …

Page 8: 02 ai-one - content analytics business cases

© ai-one inc. 2012

Query chains, combinations In certain cases it may help to chain the different

commands into a small workflow.

Use of the : combine the commands

Depending the project, its best

to combine the ai-one

commands. In the beginning on

has to switch commands

structures and conventional

thinking, but then programmers

are in our world very fast.

KeyWordCommand

ResultSet 1

Check-Asso

ResultSet / Edit

Match/Classify ResultSet 2

Check-Phonetic

Page 9: 02 ai-one - content analytics business cases

KeyWordCommand

AssoAnalyseCommant

PhoneticCommand

Learn & Tighten Commands

Focus & others…

… explain the entire semantic world!

Summary: just a few commands

© ai-one inc. 2012

Page 10: 02 ai-one - content analytics business cases

© ai-one inc. 2011

current linguistics and semantic solutions

works only if they are feed with accurate and

detailed language dependent models, and there

is NO incrementally updating/learning possible!

© ai-one inc. 2010

ai-one gives Better results!

ai-one solved this challenge, ai-one’s approach

works incrementally, shows the inherent

(intrinsic) semantic in any language without pre

programming or compelling use of ontologies

and thesauri.

Page 11: 02 ai-one - content analytics business cases

© ai-one inc. 2011© ai-one inc. 2010

LWO: Dynamic and self detection ontologies

Prof. Dr. habil. Ulrich Reimer, University of

Applied Sciences St. Gallen, "Learning a

Lightweight Ontology for Semantic Retrieval

in Patient-Center Information Systems".

One direct benefit and resulting application, explained

also in the paper of Prof Dr. habil Ulrich Reimer is, a

trend barometer that uses the ai-one core technology to

observes and analyze for example the Internet (news

platforms, online news, RSS feeds, blogs etc.) The

trend barometer finds in context and topics discussed

the current keywords, that is the semantic trends, and

builds a dynamic ontology on a daily basis. Similarly, ai-

one can be applied as trend barometer or analysis tool

on documents or databases. This opens up the

possibility to compare documents, even databases, as

regards content. The number of possible applications

are almost infinite.

Intelligent Language Handling

Page 12: 02 ai-one - content analytics business cases

© ai-one inc. 2011© ai-one inc. 2010

Full-fledged ontologies [Supervised learning]

- Works only with detailed models

- Language dependent,

- no incrementally updating

Sharing / reuse of ontologies [limited possibilities]

- Based on models and reservations about the quality

- Language dependent

- no incrementally updating

Folksonomies [WEB 2.0 / semantic WEB]

- No controlled quality or validation

- Often incomplete or not existent, Language dependent

- no incrementally updating

Intrinsic semantic (LWO) vs.:

Page 13: 02 ai-one - content analytics business cases

© ai-one inc. 2011© ai-one inc. 2010

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ai-one is language independent

The CORE works on binary level

Page 14: 02 ai-one - content analytics business cases

© ai-one inc. 2011© ai-one inc. 2010

• Categorize content

based on rules

• Structured approach

• Trained; Manually

updated and developed

Find connections

(LWO)

• Autonomous display of

any kind of data

• Unstructured approach

• Recognition of all

connections between

words

Categorization

(NLP)

Combine ai-one with NLP and ontology for best possible output conditioning.

Better decisions because ai-one!

Sense,

Meaning

Decisions

ai-one plus NLP for perfect results

Page 15: 02 ai-one - content analytics business cases

• Sentiment analyses

• Social media analyses

• Trend studies

• Automatic classifying

• Autonomic sense making

• Detect unknown patterns

• Answers unknown questions

• Autonomic decision making

.. and much more

© ai-one inc. 2012

A few ai-one commands solve

and support:

Page 16: 02 ai-one - content analytics business cases

Explore and then Explain the world of

language with basically two main

commands and a few complementary

commands:

…that’s the ai-one LIB & API!

Only a few commands are need to:

© ai-one inc. 2012

Page 17: 02 ai-one - content analytics business cases

Thank You!

© ai-one inc. 2012

ai-one inc. 5711 La Jolla Blvd.,

Bird Rock

La Jolla, CA 92037

[email protected]

www.ai-one.com

ai-one agFlughofstrasse 55,

Zürich-Kloten

8152 Glattbrugg

ai-one gmbhKoenigsallee 35a,

Grunewald

14193 Berlin