Search Leeds Talk - Entities, Search, and Rank Brain: How it works and why it matters

#SearchLeeds @schachin Kristine Schachinger
Data, Data, Everywhere.
Kristine@SitesWithoutWalls.com

In the beginning, there was a …
Large-Scale Hypertextual Web Search Engine

What?

Link Profiles
http://infolab.stanford.edu/pub/papers/google.pdf

The Web 1998

Google Goes To Work
http://infolab.stanford.edu/pub/papers/google.pdf

In 2018 …
Roughly half of the world's population or
3.8 billion people use the internet every day.

Google processes
TRILLIONS of queries a year
& has indexed BILLIONS of Websites.

IN 2015, THERE WERE
2,834,650,000,000 Google searches with
an average 7,766,000,000 searches a day.

Breaks down to …
5.5 billion searches per day
or over 63,000 search queries per second.

That is a lot of data.
#DIDYOUNEEDASLIDEFORTHIS?

Dealing With Data.

Google was founded on unstructured data.

Unstructured data (or unstructured information) is information that
either does not have a pre-defined data model or is not organized in a
pre-defined manner. Unstructured information is typically text-heavy,
but may contain data such as dates, numbers, and facts as well.
https://www.google.co.uk/search?q=definition+unstructured+data&oq=definition+unstructured+data&aqs=chrome..69i57j0l5.5175j0j7&sourceid=chrome&ie=UTF-8

Unstructured Data
uses keywords.
https://moz.com/blog/7-advanced-seo-concepts

TF-IDF
Term Frequency Inverse
Document Frequency
ie the frequency of keywords

As queries number in the trillions
unstructured data becomes inefficient.
Data needs structure.

So Google moved from
Relational Databases to
Knowledge Graphs.
Knowledge Graphs

NOTE
Knowledge Graphs
DO NOT EQUAL
THE KNOWLEDGE GRAPH
Knowledge Graphs

Graph-based knowledge representation has been researched for
decades and the term knowledge graph does not constitute a new
technology. Rather, it is a buzzword reinvented by Google and
adopted by other companies and academia to describe different knowledge
representation applications.
Knowledge Graphs
http://ceur-ws.org/Vol-1695/paper4.pdf

Enter Semantic Search
https://web.archive.org/web/20090516213508/http://blog.searchenginewatch.com/090512-201139

What is Semantic Search?

Semantic Search
=
Understanding Intent

Welcome G Squared

Google Squared
Google Squared returns search results in a spreadsheet format. It
structures the unstructured data on web pages. So a search for Small
Dogs returns results with names, description, size, weight, origin,
etc., in columns and rows.
Google is looking for data structures on the web that imply facts, and
then grabbing it for Squared results. “It takes an incredible amount of
compute power to create one of those squares,” she says.”
~Techcrunch
https://techcrunch.com/2009/05/12/what-is-google-squared-it-is-how-google-will-crush-wolfram-alpha-exclusive-video/

https://searchengineland.com/up-close-google-squared-19313
Before the Knowledge Graph

Google Squared
“Check out this example below, a Square for the search “dog breeds.” It’s cool that you can
add major or minor medical concerns to the list of columns, but the selection of examples is
really strange.
Call it structured data if you like,
I call it a surefire recipe for making a bad dog buying decision.”
https://readwrite.com/2009/06/03/google_squared_is_live_who_knew_structured_data_co/

Google Kills Google Squared.
RIP Google Squared 2009-2011

”…quite possibly ...
one of Google's significant achievements”
Nathania Johnson of Search Engine Watch
Knowledge Graphs

Why?

“Strings to Things"
The Holy Grail of Search?
NLP (Natural Language Processing).

But Google doesn’t process
Natural Language.

However, this was the early stages of Google
moving search from strings (unstructured data)
or the “bag of words” approach
 to “things” (structured data).

“Things” are known objects
with known (or learned) relationships.

Before THE Knowledge Graph – Wonder Wheel

Before the Knowledge Graph – Wonder Wheel

Welcome THE Knowledge Graph 2012.

Knowledge Graphs are based on known relationships.
THE Knowledge Graph is Google’s graph database.
THE Knowledge Graph

The Knowledge Graph (Google) is seeded by things known.
Instead of just text without meaning, The KG is a relational
graph with known objects and mapped relationships.
THE Knowledge Graph

"Four years ago this July, Google acquired Metaweb,
bringing Freebase and linked open data to Google," he wrote.
Google software engineer Barak Michener
http://www.eweek.com/database/google-releases-cayley-open-source-graph-database
THE Knowledge Graph Seeds

Also includes trusted sources such as the
CIA Fact Book, Wikipedia, Wikidata etc.
http://www.eweek.com/database/google-releases-cayley-open-source-graph-database
THE Knowledge Graph Seeds

Why the Knowledge Graph?
To help better match user intent.
THE Knowledge Graph

The Knowledge Graph enables you to search for things, people or places
that Google knows about—landmarks, celebrities, cities, sports teams,
buildings, geographical features, movies, celestial objects, works of art
and more—and instantly get information that’s relevant to your query
THE Knowledge Graph

In other words
NOUNS
THE Knowledge Graph

NOUNS=ENTITIES
THE Knowledge Graph

Google moves to
ENTITY SEARCH
THE Knowledge Graph

Knowledge Graph entities
The Knowledge Graph has millions of entries that describe real-world entities like people, places, and things. These
entities form the nodes of the graph.
The following are some of the types of entities found in the Knowledge Graph:
Book
BookSeries
EducationalOrganization
Event
GovernmentOrganization
LocalBusiness
Movie
MovieSeries
MusicAlbum
MusicGroup
MusicRecording
Organization
Periodical
Person
Place
SportsTeam
TVEpisode
TVSeries
VideoGame
VideoGameSeries
WebSite

Entities + Relationships=
Knowledge Graph
THE Knowledge Graph

Knowledge Graph
= the Answer Engine
THE Knowledge Graph

Google as an Answer Engine
https://www.google.com/search/howsearchworks/responses/#?modal_active=none

Hummingbird  “Strings to Things”.

Hummingbird
The name was derived from
the speed and accuracy of the hummingbird.

Hummingbird Arrives 2013
Google moves from matching keyword terms to
Google trying to process Natural Language Queries.

Hummingbird adds a semantic layer to the search
algorithms utilizing structured data.

Hummingbird adds a
semantic layer to the
search algorithms like
synonyms and close
variants.

Hummingbird adds a
search algorithms that
uses “semantic distance
and term relationships”.

Hummingbird adds a
search algorithms that
uses “phrase based
Indexing and co-
occurrence.”

Page Segmentation.
This part of the
algorithm determines
meaning through
placement.

Entity Salience.
This part of the
algorithm determines
meaning through known
relationships.

Google doesn’t process
Natural Language.
This means we must add an “interpreter”.

Enter Structured Data & Schema

What is Structured Data?

Structured data for SEO purposes is on-page markup that
enables search engines to better understand the information
currently on your site’s web pages, and then use this information
to improve search results listing by better matching user intent.

This structured data is defined by using schema to act as the
interpreter. This is the definition we add to the page using
schema code.
Google allows 3 types.
• RDFa
• Microdata
• JSON-LD

Schema
JSON-LD is the recommended schema code.
JSON-LD stands for JavaScript Object Notation for Linked Data
This is just a way to implement schema outside the HTML mark-up
structure. RDFa and Microformats required the code to be implemented
via HTML.

Schema
Benefit is it can be removed from the HTML structure, which
makes it easier to write, implement, and maintain.
For a good breakdown on what JSON is at the code level.
Portent’s JSON Implementation Guide is very helpful.
https://www.portent.com/blog/seo/json-ld-implementation-guide.htm

JSON-LD Schema

Schema
IMPORTANT! Test your JSON-LD.
Use the Google Structured Mark-Up Helper.
https://search.google.com/structured-data/testing-tool

Schema
NOTE this tool only tells you if it is semantically correct, NOT if
you are using the proper schema.
Make sure to check with Google’s Guides on schema
implementation. Improper use or implementation can result in
a manual action.
• https://developers.google.com/search/docs/guides/intro-structured-data
• https://developers.google.com/search/docs/guides/prototype

Schema
IMPORTANT! Your JSON content MUST match what is in the
page exactly. If they differ, you will likely get a manual action as
Google sees this as cloaking.

Schema

Ranking Without Links

Rank Brain

Rank Brain is used for Unknown Queries where entity
meanings/relationships are unclear or unknown.

Rank Brain.
Only algorithm that uses AI
on the live results
Rank Brain.

Presence of Rank Brain means
Google is confused …

Why?
Google does not use NLP
(Natural Language Processing)
Rank Brain.

Uses Structured Data, Entities & Known Relationships
Person, Place, Thing = Noun = Entities.
Nouns or Persons/Places/People/Things are what we call entities. Entities are
known to Google and their meaning is defined in the databases Google references.
Rank Brain.

Rank Brain Also Uses Users Queries & Clicks
to Help It Understand Query Intent.

Should you optimize for it?
Rank Brain.

Why would you optimize to rank with AI?
Rank Brain.

(Gary Illyes)
Rank Brain.

Just write in natural and conversational language.

So why do we care about this?

We can act as the interpreter and help “teach”
Google what our site is about.

Adding semantic mark-up
(structured data via schema) allows us to tell
Google what WE SAY our site is about and WHAT
RELATIONSHIPS we define within it.

We can act as the interpreter and help “teach”
Google the context of our content.

We can help give Google a clearer understanding.
And in the end that helps us help Google better
answer the questions users ask and to better
surface our content where we want it.
We give our data meaning Google understands.

Search Leeds Talk - Entities, Search, and Rank Brain: How it works and why it matters

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Search Leeds Talk - Entities, Search, and Rank Brain: How it works and why it matters