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Bill Slawski 
SMX East 2014 (#smx #21A) 
October 1, 2014 (9:00am-10:15am)
When Sergey Gave Larry a Tour 
“We both found each other obnoxious,” Brin counters 
when I tell him of Page's response. "But we say it a 
little bit jokingly. Obviously we spent a lot of time 
talking to each other, so there was something there. 
We had a kind of bantering thing going." Page and 
Brin may have clashed, but they were clearly 
drawn together - two swords sharpening one 
another. 
The Birth of Google by John Battelle #smx #21A @bill_slawski
#smx #21A @bill_slawski
Larry Invents PageRank 
Improved Text Searching in Hypertext Systems (pdf) #smx #21A @bill_slawski
#smx #21A @bill_slawski
Sergey Invents DIPRE 
Extracting Patterns and Relations from the World Wide Web (pdf) 
#smx #21A @bill_slawski
#smx #21A @bill_slawski
Patterns! 
#smx #21A @bill_slawski
Andrew Hogue’s Team 
Andrew Hogue’s Resume #smx #21A @bill_slawski
Patterns! 
#smx #21A @bill_slawski
Gathering & Annotating Knowledge 
Browseable fact repository #smx #21A @bill_slawski
Search becomes Knowledge 
#smx #21A @bill_slawski
Google’s Knowledge Graph 
The Knowledge Graph #smx #21A @bill_slawski
#smx #21A @bill_slawski
Alexandria torpedo factory CC BY-SA 3.0 #smx #21A @bill_slawski
Google Starts a Conversation 
FAQ: All About The New Google “Hummingbird” 
Algorithm 
#smx #21A @bill_slawski
Entities become “Search Entities” 
Search entity transition matrix and applications of the transition matrix 
Relationships between Search Entities 
#smx #21A @bill_slawski
Which [lincoln]? 
#smx #21A @bill_slawski
Tracking Knowledge Information 
Each record (herein referred to as a tuple: <document 
, query, data> ) comprises a query submitted by 
users, a document reference indicating the document 
selected by users in response to the query, and an 
aggregation of click data for all users or a subset of all 
users that selected the document reference in response 
to the query. 
Propagating query classifications 
Using Query User Data to Classify Queries 
#smx #21A @bill_slawski
Google is 
 Viewing Entities as Search Entities (“lincoln as a 
person” is a query) 
 likely using an RDF (Resource Description Framework) 
schema for tracking information to calculate 
probabilities (based on user behavior) of things like 
what classification is meant by a query. 
 Searching for Patterns in Queries and on Pages to 
answer questions 
 Looking for Schema markup and schema-related 
facts and attributes information to create and 
understand context. 
#smx #21A @bill_slawski
Patterns! 
#smx #21A @bill_slawski
Query Revision Based on Context 
and Substitute Rules 
Synonym identification based on co-occurring terms #smx #21A @bill_slawski
For example, the user may enter the search query "What is 
the best place to find and eat Chicago deep dish style 
pizza?" 
In determining whether the term "restaurant" is a synonym 
for the query term "place", a synonym engine may evaluate 
the query term in the context of adjacent terms, such as 
"best" or "to," as well as non-adjacent terms, such as 
"Chicago" and "pizza." 
Such an evaluation may result in the decision that, in the 
context of the non-adjacent term "pizza," the term 
"restaurant" is a synonym of the query term "place." 
#smx #21A @bill_slawski
Knowledge Base Searches 
Identifying entities using search results #smx #21A @bill_slawski
The Future of the Knowledge 
Graph? 
Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion 
#smx #21A @bill_slawski
Incompleteness of Knowledge 
Graph 
#smx #21A @bill_slawski
Introducing the Knowledge Vault? 
Constructing and Mining Web Scale Knowledge #smx #21A @bill_slawski
Recovering Semantics of Tables on the Web (pdf) 
#smx #21A @bill_slawski
Open Language Information 
Extraction 
Open Language Learning for Information Extraction (PDF) 
#smx #21A @bill_slawski
<div itemscope itemtype ="http://schema.org/Movie"> 
<h1 itemprop="name"&g;Avatar</h1> <div 
itemprop="director" itemscope 
itemtype="http://schema.org/Person"> Director: 
<span itemprop="name">James Cameron</span> 
(born <span itemprop="birthDate">August 16, 
1954)</span> </div> <span itemprop="genre">Science 
fiction</span> <a href="../movies/avatar-theatrical-trailer. 
html" itemprop="trailer">Trailer</a> </div> 
Getting started with schema.org #smx #21A @bill_slawski
Quizz: Targeted Crowdsourcing with a Billion (Potential) Users (pdf) 
#smx #21A @bill_slawski
Search + Knowledge, Sharpening 
One Another 
#smx #21A @bill_slawski
Thank you - Bill Slawski 
 Director of Search Marketing at GoFishDigital 
 Author at SEO by the Sea 
 Tweet me at https://twitter.com/bill_slawski 
 About this session (#smx #21A) 
 Connect Author Entities: 
https://plus.google.com/u/1/+BillSlawski/posts 
 Email me: bill.slawski@gofishdigital.com 
#smx #21A @bill_slawski

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Hummingbird & the entity revolution

  • 1. Bill Slawski SMX East 2014 (#smx #21A) October 1, 2014 (9:00am-10:15am)
  • 2. When Sergey Gave Larry a Tour “We both found each other obnoxious,” Brin counters when I tell him of Page's response. "But we say it a little bit jokingly. Obviously we spent a lot of time talking to each other, so there was something there. We had a kind of bantering thing going." Page and Brin may have clashed, but they were clearly drawn together - two swords sharpening one another. The Birth of Google by John Battelle #smx #21A @bill_slawski
  • 4. Larry Invents PageRank Improved Text Searching in Hypertext Systems (pdf) #smx #21A @bill_slawski
  • 6. Sergey Invents DIPRE Extracting Patterns and Relations from the World Wide Web (pdf) #smx #21A @bill_slawski
  • 8. Patterns! #smx #21A @bill_slawski
  • 9. Andrew Hogue’s Team Andrew Hogue’s Resume #smx #21A @bill_slawski
  • 10. Patterns! #smx #21A @bill_slawski
  • 11. Gathering & Annotating Knowledge Browseable fact repository #smx #21A @bill_slawski
  • 12. Search becomes Knowledge #smx #21A @bill_slawski
  • 13. Google’s Knowledge Graph The Knowledge Graph #smx #21A @bill_slawski
  • 15. Alexandria torpedo factory CC BY-SA 3.0 #smx #21A @bill_slawski
  • 16. Google Starts a Conversation FAQ: All About The New Google “Hummingbird” Algorithm #smx #21A @bill_slawski
  • 17. Entities become “Search Entities” Search entity transition matrix and applications of the transition matrix Relationships between Search Entities #smx #21A @bill_slawski
  • 18. Which [lincoln]? #smx #21A @bill_slawski
  • 19. Tracking Knowledge Information Each record (herein referred to as a tuple: <document , query, data> ) comprises a query submitted by users, a document reference indicating the document selected by users in response to the query, and an aggregation of click data for all users or a subset of all users that selected the document reference in response to the query. Propagating query classifications Using Query User Data to Classify Queries #smx #21A @bill_slawski
  • 20. Google is  Viewing Entities as Search Entities (“lincoln as a person” is a query)  likely using an RDF (Resource Description Framework) schema for tracking information to calculate probabilities (based on user behavior) of things like what classification is meant by a query.  Searching for Patterns in Queries and on Pages to answer questions  Looking for Schema markup and schema-related facts and attributes information to create and understand context. #smx #21A @bill_slawski
  • 21. Patterns! #smx #21A @bill_slawski
  • 22. Query Revision Based on Context and Substitute Rules Synonym identification based on co-occurring terms #smx #21A @bill_slawski
  • 23. For example, the user may enter the search query "What is the best place to find and eat Chicago deep dish style pizza?" In determining whether the term "restaurant" is a synonym for the query term "place", a synonym engine may evaluate the query term in the context of adjacent terms, such as "best" or "to," as well as non-adjacent terms, such as "Chicago" and "pizza." Such an evaluation may result in the decision that, in the context of the non-adjacent term "pizza," the term "restaurant" is a synonym of the query term "place." #smx #21A @bill_slawski
  • 24. Knowledge Base Searches Identifying entities using search results #smx #21A @bill_slawski
  • 25. The Future of the Knowledge Graph? Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion #smx #21A @bill_slawski
  • 26. Incompleteness of Knowledge Graph #smx #21A @bill_slawski
  • 27. Introducing the Knowledge Vault? Constructing and Mining Web Scale Knowledge #smx #21A @bill_slawski
  • 28. Recovering Semantics of Tables on the Web (pdf) #smx #21A @bill_slawski
  • 29. Open Language Information Extraction Open Language Learning for Information Extraction (PDF) #smx #21A @bill_slawski
  • 30. <div itemscope itemtype ="http://schema.org/Movie"> <h1 itemprop="name"&g;Avatar</h1> <div itemprop="director" itemscope itemtype="http://schema.org/Person"> Director: <span itemprop="name">James Cameron</span> (born <span itemprop="birthDate">August 16, 1954)</span> </div> <span itemprop="genre">Science fiction</span> <a href="../movies/avatar-theatrical-trailer. html" itemprop="trailer">Trailer</a> </div> Getting started with schema.org #smx #21A @bill_slawski
  • 31. Quizz: Targeted Crowdsourcing with a Billion (Potential) Users (pdf) #smx #21A @bill_slawski
  • 32. Search + Knowledge, Sharpening One Another #smx #21A @bill_slawski
  • 33. Thank you - Bill Slawski  Director of Search Marketing at GoFishDigital  Author at SEO by the Sea  Tweet me at https://twitter.com/bill_slawski  About this session (#smx #21A)  Connect Author Entities: https://plus.google.com/u/1/+BillSlawski/posts  Email me: bill.slawski@gofishdigital.com #smx #21A @bill_slawski

Editor's Notes

  1. 1999 paper by Sergey Brin was also filed as a provisional patent, and updated at least three times since in continuation patents, including a recent filing.
  2. This new Knowledge division when Lawrence page became CEO was announced quietly in one of Google’s financial documents.