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

3.
#smx #21A @bill_slawski

4.
Larry Invents PageRank
Improved Text Searching in Hypertext Systems (pdf) #smx #21A @bill_slawski

5.
#smx #21A @bill_slawski

6.
Sergey Invents DIPRE
Extracting Patterns and Relations from the World Wide Web (pdf)
#smx #21A @bill_slawski

7.
#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

14.
#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