In Search of Natural Language Processing: Rank Brain, Google, SEO, and You.

#Ungagged #Vegas @schachin Kristine Schachinger
In Search of NLP
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 …
7.7 billion average searches per day
or over 63,000 search queries per second.

Dealing With The Data.

Google Search
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

Keywords.

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.

Keywords to Queries.
Welcome Semantic Search!

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
and TensorFlow
https://web.archive.org/web/20090516213508/http://blog.searchenginewatch.com/090512-201139

https://www.tensorflow.org/guide/
MACHINE LEARNING

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.” ~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
“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

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

Why?

The Holy Grail
of Search?
NLP
(Natural Language
Processing)

“Strings to Things"
But Google doesn’t process
Natural Language.

G-Squared 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

THE Knowledge Graph Seeds.

"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.
To understand what users want.
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
THE Knowledge Graph ENTITIES

Entities + Relationships=
THE 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.

But Google doesn’t process
Natural Language very well.

Moving to vectors.

KEY FACTOR word2vec:
Vector space models (VSMs) represent (embed)
words in a continuous vector space where
semantically similar words are mapped to nearby
points ('are embedded nearby each other').
Hummingbird
https://www.tensorflow.org/tutorials/representation/word2vec

Embedded Word Model
Hummingbird

“…words that appear in the same contexts share semantic meaning. The
different approaches that leverage this principle can be divided into two
categories: count-based methods (e.g. Latent Semantic Analysis),
and predictive methods (e.g. neural probabilistic language models).”
Hummingbird

Hummingbird

Hummingbird added a semantic layer
to the search algorithms.

Semantic Interpretations.

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.

So Hummingbird moves from
strict word count based modeling
(ie keyword counts) to
probabilistic modeling
(ie predictive interpretation)
via known word vectors.
Hummingbird

What does this look like?
From Google’s Natural Language Cloud Tool

What does this look like?

What does this look like mathematically?

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

Structured Data and 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

Why Does Schema Matter?

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.
That helps us help Google better answer
the questions users ask
and to better surface our content for those users
We give our data meaning
Google Understands

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 in Search
(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.

• Words go in.
• Words get assigned a mathematical address in a vector.
• Similar and related words sit close to each other in the vector space.
• Words are retrieved based on your query and the words it locates in the “best fit” vector.
• These word “interpretations” are used to return results.
• If the relationships are weak or unknown, enter Rank Brain.
• Behind the scenes, data is continually fed into the machine learning process, so as to make
those results more relevant the next time.
Rank Brain – Known Relationships.

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.

Google Does Not Even Understand What
Rank Brain is Actually Doing.
Rank Brain.

(Gary Illyes)
Rank Brain.

Just write in natural and conversational language.
Create holistic content.

Write holistic content.
Use terms that are semantically related.
For a detailed explanation Google explains here > https://www.youtube.com/watch?v=vzoe2G5g-w4&feature=youtu.be&t=32m19s

Write holistic content.
DOES YOUR CONTENT HAVE DEPTH AND WIDTH?
For a detailed explanation Google explains here > https://www.youtube.com/watch?v=vzoe2G5g-w4&feature=youtu.be&t=32m19s

Takeaways.
• Think Search Queries NOT Simple Keywords
• Write in natural, conversational language
• Write using holistic content
• Focus on depth and breadth with related terms
• Add Structured Data
Takeaways.

THINK in Query Terms & Context.

https://cloud.google.com/natural-language/

In Search of Natural Language Processing: Rank Brain, Google, SEO, and You.

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to In Search of Natural Language Processing: Rank Brain, Google, SEO, and You.

Similar to In Search of Natural Language Processing: Rank Brain, Google, SEO, and You. (20)

More from Kristine Schachinger SEO and Online Marketing

More from Kristine Schachinger SEO and Online Marketing (13)

Recently uploaded

Recently uploaded (20)

In Search of Natural Language Processing: Rank Brain, Google, SEO, and You.