The document summarizes a presentation about using text analytics and semantic technologies to improve enterprise search capabilities. It discusses how internal and external information is growing faster than companies can manage, and how business expect search solutions to capture the hidden value in strategic information. It then demonstrates how semantic analysis can dynamically enrich content with relevant metadata to create contextually accurate searches, as seen in the integration of a semantic platform with the Google Search Appliance. The presentation explains how deep linguistic analysis can establish word context and disambiguate terms, which is then deployed to improve search results.

1. Text Analytics World
San Francisco – March 31, 2015
4:15-4:45pm
Speaker: Bryan Bell, Executive Vice President, Expert System USA
 What is in Your Business Requirement: Searching or Finding? Enterprise Search
 Product Demonstration: The Google Search Appliance (GSA) integrated with a
semantic technology platform.
1. Internal and external information comes at us faster than we can keep up with.
2. Business expectations for deploying solutions, using enterprise search and content navigation systems
to capture the hidden value of strategic information.
3. CONTEXT: Exploiting deep linguistic analysis, combined with semantics offers the ability to create
contextually correct metadata.
4. Dynamically enrich content with contextually relevant metadata and deploy as the heart of a
knowledge management applications and the Google Search Appliance.

2. 1. Internal and external information comes at us faster than we can keep up with.
80 – 90% is unstructured text.

3. Zettabyte
1,000,000,000,000,000,000, 000 bytes

4. 4
 The Google crawler visits 20 billion web sites a day.
 The search engine has located more than 30 trillion unique URLs.
 Processes 100 billion searches every month.
• 3.3 billion searches per day.
• Over 38,000 thousand searches per second.
• A single Google query uses 1,000 computers to retrieve an answer.
• This volume combined with the PageRank algorithm…
PR(A) = (1-d) + d (PR(T1)/C(T1) + PR(Tn)/C(Tn)) …. is why Google is so good on the internet.
• 16% to 20% of queries that get asked every day have never been asked before.
Amit Singhal,
Senior Vice President of development, Google Search
August 2012
The Internet

5. 2. Deploying internal enterprise search
engine / content navigation system to
capture and share the hidden value of the
information that is available to the company.
The intranet / corporate portal

6. 2. Deploying internal enterprise search
engine / content navigation system to
capture and share the hidden value of the
information that is available to the company.
The intranet / corporate portal

7. “Our search stinks!
I want it to work
like Google.”

9. 9
Zettabyte
1,000,000,000,000,000,000,000 bytes
Good news:
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
Don’t have 3.3 billion searches per day.
Don’t have 38,000 thousand searches per second.
Don’t have 1,000 computers to retrieve an answer.

10. 10
Zettabyte
1,000,000,000,000,
000,000,000 bytes
Key words
No metadata
Poor metadata
Inconsistent

11. 11
Zettabyte
1,000,000,000,000,
000,000,000 bytes
Key words
No metadata
Poor metadata
Inconsistent
=
POOR
CONTENT
FINDABILITY

12. 12
stock
People are able to disambiguate “on the fly”, but machines cannot.
Key words vs. Context
Language ambiguity

13. 13
People are able to disambiguate “on the fly”, but machines cannot.
stock
apple
Key words vs. Context
Language ambiguity

14. 14
stock
apple
Apple
People are able to disambiguate “on the fly”, but machines cannot.
Key words vs. Context
Language ambiguity

15. 15
stock
apple
Apple
“I bought 10,000 shares of stock in Apple.”
“I have 10,000 apples in stock.”
People are able to disambiguate “on the fly”, but machines cannot.
Context is King

16. 3. Exploiting deep linguistic analysis,
combined with semantics.
4. Dynamically enrich content with contextually
relevant metadata.
How is word context established?

17. Morphological
analysis word forms dog, dog-catcher, doggy bag
Grammatical analysis parts of speech "There are 40 rows in the table." (noun)
"She rows 5 times a week." (verb)
Logical analysis
word
relationships
"The car I bought, to replace my Chrysler,
stinks."
Semantic analysis word context "I bought 10,000 shares of stock in Apple."
"I have 10,000 apples in stock."
"I used chicken broth for my soup stock."
Deep linguistic analysis of words to achieve word disambiguation.
How is word context established
and deployed with the GSA?

18. www.intelligenceapi.com

19. 20
Linguistic and semantic analysis engine

20. 27
Case Study: GSA – Google Search Appliance
What is in Your Business Requirement? Searching or Finding.

21. Contacts
Thank you
Bryan Bell
bbell@expertsystem.com
@bellbryan
+1.847.508.7938
www.expertsystem.com