A look at how Entities and Concepts have grown to become part of Google's Search Results

1. SEO and the New Search Results – A
look at how Entities and Concepts have grown to
become part of Google’s Search Results
DFWSEM
March 9, 2016
NOAH’s Event Center
2251 N Greenville Ave
Richardson, TX 75083

2. About Bill Slawski
• Director of Search
Marketing at Go Fish
Digital
• Founder of SEO by
the Sea
• Co-Host of Bill and
Ammon’s Bogus
Hangout

3. Searchers are looking for Entities…
According to an internal study of Microsoft, at
least 20-30% of queries submitted to Bing
search are simply name entities, and it is
reported 71% of queries contain name
entities.
Building Taxonomy of Web Search Intents for Name Entities

4. Andrew Hogue
Google Senior Staff Engineer and
Engineering Manager June 2004 –
November 2011:
Annotations Framework
Structured Data and NLP Strategy
Question Answering and NLP
Google Squared
Google Goggles

5. Google’s Annotation Framework
Andrew Hogue’s Resume

6. What is an Annotation?
Larry Page on the Origins of Google’s PageRank
From an entry in Brad DeLong’s journal from 2003.
Larry Page: “It wasn’t that we intended to build a search engine. We built a
ranking system to deal with annotations. We wanted to annotate the
web–build a system so that after you’d viewed a page you could click and
see what smart comments other people had about it. But how do you
decide who gets to annotate Yahoo? We needed to figure out how to
choose which annotations people should look at, which meant that we
needed to figure out which other sites contained comments we should
classify as authoritative. Hence PageRank.
“Only later did we realize that PageRank was much more useful for search
than for annotation…”
(via @kevinmarks)

7. Browseable Fact Repository
Browseable fact repository
https://www.google.com/patents/US7774328
Granted Aug 10, 2010
Filing date Feb 17, 2006
Inventors Andrew W. Hogue, Jonathan T. Betz
Original Assignee Google Inc.

8. Google Acquired Metaweb in 2010

9. Google’s New Head of Search
•Co-Founder of MetaWeb
•Former Head of Machine Learning
at Google
•Inventor of Query Optimization
Patent

10. Query Optimization Patent
For example,
9202a8c04000641f8000000000006567, is the
GUID which corresponds to the person known as
"Arnold Schwarzenegger". The front part,
9202a8c04000641f8, is the database id and
the back part, 6567, is the primitive id.
Query Optimization US 20110093500 A1, by Scott Meyer,
Jutta Degener, Barak Michener, John Giannandrea

11. MetaWeb Query Optimization

12. Machine ID Number in Freebase
“A mid or machine id is a short form of id for any Freebase topic. Mids
allow topics to be more easily managed and changes tracked over time,
particularly when they are stored in external data sources and used as
foreign keys into Freebase. They differ from a guid in being shorter, and
in magically working in spite of topic merges and other such
transformations.”
~ http://wiki.freebase.com/wiki/Machine_ID

13. Type: Body Builder

14. Type: Actor

15. Type: Actor

16. Type: Actor

17. Type: Politician

18. Other Uses of MIDs at Google
• Reverse Image Search
• Trends
• Knowledge Graph API
• Searches of Nearby Entities (Shortly!)

19. Reverse Image Search uses MIDs
As in ImageNet, the classes were not text strings, but are
entities, in our case we use Freebase entities which form
the basis of the Knowledge Graph used in Google search.
An entity is a way to uniquely identify something in a
language-independent way. In English when we encounter
the word “jaguar”, it is hard to determine if it represents
the animal or the car manufacturer. Entities assign a unique
ID to each, removing that ambiguity, in this case
“/m/0449p” for the former and “/m/012x34” for the latter.
Improving Photo Search: A Step Across the Semantic Gap,

20. Google Trends uses MIDs to Track*
https://www.google.com/trends/explore#q=%
2Fm%2F045c7b (unencoded: “/m/045c7b”)
Google Freebase listing (MID /m/045c7b):
*(See Barbara Starr slide 26)

21. MIDs in Google KG Search API
A Layman’s Visual Guide To Google’s Knowledge Graph Search API
What is JSON-LD? A Talk with Gregg Kellogg

22. Nearby Entities and MIDs
Example data can include a name of the entity, a
location of the entity, one or more types assigned to
the entity, one or more ratings associated with the
entity, a set of entity query patterns associated with
the entity, and any other appropriate information that
can be provided for the entity. In some examples, an
entity can be associated with a unique identifier within
the one or more databases. For example, the entity
Alcatraz Island can be assigned the identifier
/m/0h594.
Interpreting User Queries Based on Nearby Locations

23. The Magic Carpet Ride
A statute, near my home in Cardiff-by-the-Sea, known locally as
“The Cardiff Kook.” Many Locals don’t know it by its actual name

24. KG Search API
Google’s Knowledge Graph knows both names….

25. Knowledge Panel
Google Shows a Knowledge Panel on a search for its name

26. We May Be Able to Query Nearby
Entities Soon