Semantic Content Networks - Ranking Websites on Google with Semantic SEO

Semantic Content Networks
Next Step of Creating Topical Map

Basics - Index
• Knowledge Base
• Knowledge-based Trust
• Semantic Networks
• Frames
• Frame Scripts
• Concept Map
• Topic Map
• Concept Graph
• FrameNet
• Semantic Content Networks

Knowledge Base
• Knowledge-base consists of the related existences, and their
dimensions.
• A knowledge-base is different from knowledge-graph.
• Knowledge-graph is the graphical version of knowledge-base.
• A knowledge base involves the facts, or misinformation with
different labels.
• A search engine defines every website as a “knowledge-base”.
• A search engine has its own “knowledge-base” too.
• So, if we have a knowledge-base, what is a semantic network?
• Semantic Network is the different variations of connections from
the same elements of the knowledge-base.
• “X” can be defined 99 different phrase variations.
• “X” can have 6 context domain connections, 2 Knowledge Domain
connections, and 9 entities from 3 different entity types.
• For all these connections, search engine can find different
answers, or propositions as labeled different IDs. Programmable search engines
Inventor Ramanathan V. Guha
Assignee Google LLC

Knowledge Base
• Semantic Network is the different
variations of connections from the
same elements of the knowledge-
base.
• “X” can be defined 99 different
phrase variations.
• “X” can have 6 context domain
connections, 2 Knowledge Domain
connections, and 9 entities from 3
different entity types.
• For all these connections, search
engine can find different answers,
or propositions as labeled different
IDs.
Detecting spam search results for context processed search queries
Assignee Google LLC

Knowledge Base
• To understand the Semantic Search
Engines, “this year”, we will focus on
the Ramanathan V. Guha.
• “Detecting spam search results for
context processed search queries”
focus on using queries to understand
the spam documents.
• The “Product Line” and “Product
Graph” are relevant here.
• A “fake product name”, or a wrong
match of “product-brand” association
can make a web document “spammy”.
• A knowledge base is necessary to
prevent it.
Detecting spam search results for context processed search queries
Current Assignee Google LLC

Knowledge Base
• This one focuses on “aggregating
context”, rather than processing it.
• It tries to use a specific mechanism to
define the knowledge of facts.
• “Query Definitions, Aspects and
Rephrasification” are relevant here.
• A knowledge base can be used to
understand overall context of a specific
entity.
• A search engine can filter pages based
on their “context hints”, like design,
structured data, relational layout
elements, or currencies, statistics,
measurement units, and brands,
products, or other types of concepts.
Aggregating context data for programmable search engines
Assignee Google LLC

Knowledge Base
• A search engine, naturally, can
classify the search results.
• The thing here is that Ramanathan V.
Guha focuses on a knowledge-base
like “representation of things” to
classify the documents.
• There are many methods for
document classification.
• But, it categorizes query, if the
ranking is good, it assumes that the
document should be labeled with a
category.
• A knowledge base is necessary to
classify the query, and query
classification is necessary for
document classification.
Classifying search results
Assignee Google LLC

Knowledge Base
• Search Result Ranking Based on Trust
focuses on “factuality” and “accuracy” of a
source.
• It tries to rank documents based on the
comprehensiveness.
• Not a surprise to see “cancer” as an
example on the patent.
• It explains the trust signals.
• It focuses on “vertical knowledge domain
websites” for further trust signal
information.
• He tries to use words around the links for
labeling them.
• Negative reviews are thought to be used
as well.
Search result ranking based on trust
Inventor Ramanathan V. GuhaCurrent
Assignee Google LLC

Knowledge Base
• “Query Identification” is different from
“Query Aspects” and “Query Definitions”.
• An early example of the “query
categorization” and “query-category
map”.
• It uses the “query-page” matching from
the “query logs” which signals the
prominence of historical data.
• The “QP Tuples” are the “Query-Page”
Tuples.
• It uses “query logs”, thus, if you are not in
the query logs despite your quality, you
can be overridden.
• Knowledge Base Relevancy: It has to
categorize the things on the queries and
documents to increase the precision.
Ramanathan V. Guha, Shivakumar Venkataraman, Vineet GuptaGokay,
Baris Gultekin, Pradnya Karbhari, Abhinav Jalan
Query identification and association
Inventor Assignee Google LLC

Knowledge Base
• Generating specialized
search results just for a
pattern of queries.
• I focused on “question-
query-document”
templates last year.
• So, I will skip it.
• But, it uses “knowledge
base” for understanding
the data representation.
Generating specialized search results in response to patterned queries

Knowledge Base
• It is a good feeling to follow Mr.
Ramanathan.
• Because, he tries to find answers for
the important questions.
• “Corroboration of Web Answers”.
• Co-identification of objects across
heterogeneous information sources is
prominent, because it provides a
“cross-check” for the facts.
• The ranking search results based on
trust, and “co-identification of
objects” should be handled together.
• Query-augmentation is processed in
this patent by “replacing predicates”.
• Predicate is the heaviest term in the
query.
• Thus, “pattern matching” is relevant
here too.
Heuristic co-identification of objects across heterogeneous information
sources

Knowledge Base
• Providing search results is an
important patent for the
basics.
• It explains the “entity types”,
attributes, and HTML Code
connections.
• It focuses on the “templates”
for specific situations.
• The examples are for
“restaurants”.
• Thus, in the next slides,
remember the concept,
“frame script”.
Providing search results

Knowledge Base
• Most of the basic Google
functions are processed by
Ramanathan V. Guha.
• This is important, because it
makes a differentiation
between “worthy result”,
and “regular result”.
• A worthy result is a signal for
the quality.
• Even, Google Alert, today,
can be used to find some
“worthy” results.
Customized web summaries and alerts based on custom search
engines

Who is Ramanathan V. Guha? Why do we
focus on him?
• He is important.
• Creator of Schema.org.
• Creator of RSS.
• Creator of RDF.
• He is a “Google Fellow”
means “distinguished
engineer”.
• He has been cited in many
books.

focus on him?
• Semantic Web, Making
Sense, and other specific
organizations or event
cited him regularly.
• He mainly focuses on
“Contexts”.

focus on him?
• He has written a book.
• He published many speeches
about Google and Open Web.
• At the right, you see his book,
“Contexts – A formalization
and some applications”.
• And a screenshot from “Light
at the end” presentation.
• He explains why the “net” can’t
be owned.
• It is good to hear it from a
Google Fellow.

focus on him?
• DataCommons is important
from his speech.
• DataCommons is a
Knowledge Base.
• Google integrated the
DataCommons into their
internal search system.
• They defined the project as
below.

focus on him?
• Since, he was able to organize four
different search engines, it is a quite
big success.
• But, the main prominence of
Ramanathan is that he thought using
a “knowledge base” for every search
engine problem.
• Then, he tried to solve problem.
• We can mention Justin Boyan and
Data-highlighter tool, or Machine
Learning/Rule-based System
Transition on Google, as well.

focus on him?
• These are some of the entity types,
and their possible relation types in a
structured data sample.
• Categories for occurrence, or
categories by domains represent how
these things help search engines to
structure their own “semantic
networks” and knowledge bases.

focus on him?
• Ramanathan also worked with the
web masters.
• He visualized the structured data as a
form of “semantic network”.
• Thus, from a Knowledge Base
Concept to the Relational Databases,
he has a prominent place.
• He created some guides for SEOs too.

focus on him?
• Vocabulary represents the richness
of the content in terms of unique
words.
• But, Ramanathan uses it for
different topic grasping algorithms.
• On his talk, during 2014, he
mentioned Scholarly Works,
Comics, Serials, and Sports.
• After that, Schema.org started to
launch new SD Types.

focus on him?
Aggregated Knowledge Graph => Does it sound similar from the patents?

Knowledge Base
• This is another prominent
sample that I preferred to put
with some common inventors.
• “Alternatives of an answer, and
a question” represent the
“slight differences on wording”.
• And, it requires to have a
“knowledge base”, since it
focuses on Information
Extraction.
• Understand the difference
between “Occurrence Tracking
Search Engine”, and
“Perception based Search
Engine”.
Determining question and answer alternatives
Inventor David Smith, Engin Cinar Sahin, George Andrei Mihaila

Knowledge Base
• From the same fellows,
you have a “Context”.
• They cite the Ramanathan
in the invention.
• They mention the “Topics”
and “Movie/Book”
intersection of the Harry
Potter. Determining question and answer alternatives
Inventor David Smith, Engin Cinar Sahin, George Andrei Mihaila

Knowledge-based Trust
involves a PageRank
balancing effect via
Accurate, Unique and
Comprehensive
Information on sources.
• “Ranking search results
with Trust” via a
Knowledge Based evolved
into “Knowledge-based
Trust” in the same year.
Explained Before

involves a PageRank
balancing effect via
Accurate, Unique and
Comprehensive
Information on sources.
• A single “broken sentence”
can decrease the
granularity of the semantic
network that will be
extracted.
Explained Before

• “Extractor”, or
“Constructor”…
• It is a representation of
the trust-measurement
framework.
• The “Directed Graph”
model explains how the
“value pairs” are being
extracted.
Explained Before

• “Source Accuracy”
represent the trust that a
search engine can give for
visibility.
• The algorithm design
simply focus on two main
steps.
• Extracting the triples.
• Comparing them to the
others, especially the ones
in Knowledge Base.
Explained Before

• An example of the “Triple
Correctnes Prediction”.
• “Freebase Triple” is
important.
• Freebase is bought by
Google to forge the
Knowledge Graph.
• The “triple” from
Knowledge Base is
compared to the triples
from the SERP.
Explained Before

• Seed Sources and Tail
Sources.
• Remember the RankMerge.
• Remember the Topic-
sensitive PageRank.
• KBT is not an alternative of
PageRank.
• It is a Hybrid Search Engine
from Hypertextual to
Semantic.
Explained Before

Semantic Networks
• Semantic Networks are the knowledge
representations with the relational
databases.
• A knowledge base can have multiple
semantic networks from the same
knowledge base dimensions.
• All the knowledge representation
schemes should be human-
understandable.
• Semantic Networks and Frame
Representations are connected to each
other.

Semantic Networks
• Knowledge Bases represent the Knowledge.
• Thus, Knowledge Representation is a central
term.
• Logical Representation is “difficult for
common sense”.
• Logical Representation has uncertainty, and it
has time constraints, along with beliefs.
• Logic representations require “reasoning”.
Basically, it is “TrueVTrue = True”.
• These rules are not good for extracting the
facts, and storing them in a relational
database.
Entity ID of m076hq

Semantic Networks
• Semantic Networks represent factual
knowledge in classes of objects and
their properties.
• Declarative knowledge
representations, and their relations
are stored.
• Static representations (knowledge that
stays same) are more common for
semantic networks.
• Semantic Networks imitate human
memory.
• Structured Object Representation
include properties, nodes and edges
within Directed Graphs.
• Thus, it is called “Association Graph”. site:https://artsandculture.google.com/entity/

Semantic Networks
• General Networks shouldn’t be considered as Semantic Network.
• Every “arch”, knowledge representation has to have a relation type.
This is a general network.

• We need “relation types”.

• Lexical Parts of a Semantic Content
Network:
• Nodes – objects from different classes, or
individuals.
• Links – relationships between the objects.
• Labels – nodes and links, together.
• Procedural Part:
• Constructors
• Destructors
• Writers
• Readers
Structural Part has the “links and nodes”.
Semantic part has the “associated meanings”
based on the knowledge domain.

• Relation Type Examples: And/OR Trees
This is relevant to “Boolean Retrieval”.

• Relation Type Examples: IS/A Hierarchy Is/A relation type is relevant to the
Taxonomy, Lexical Relations,
Semantic-Dependency Tree, and
Augmented Information Retrieval.
All these are relevant to Set Theory.

• Relation Type Examples: IS/Part Hierarchy Is/A relation type is relevant to the
Taxonomy, Lexical Relations,
Semantic-Dependency Tree, and
Augmented Information Retrieval.
All these are relevant to Set Theory.
Lexical Relations help an SEO to focus on
meaning, rather than query search demand
(keyword volume.)

Semantic Networks
1. Hypernym: The general word of another word. For example,
the word color is the hypernym of red, blue, yellow.
2. Hyponym: The specific word of another general word. For
example, crimson, violet, lavender are the hyponyms of
purple. And, purple is the hyponym of the color.
3. Antonym: The opposite of another word. For example, the big
is the antonym of the small, and the early is the antonym of
the late.
4. Synonym: The replacement of another word without changing
the meaning. For example, huge is the synonym of big, and
initial is the synonym of early.
5. Holonym: The whole of a part. For example, the table is the
holonym of the table leg.
6. Meronym: The part of a entire. For example, a feather is the
meronym of a bird.
7. Polysemy: The word with different meanings such as love, as
a verb, and as a noun.
8. Homonymy: The word with different meanings accidentally,
such as bear as an animal and verb, or bank as a river or
financial organization.

Semantic Networks
Inheritance helps search engines to Extract Information
by augmenting.
Semantic Dependency Tree is a representation of
inheritance.
Source: Ryan J. Urbanowicz.

Semantic Networks
Every arch defines a binary relation.
For the sentence, “John’s mother has
sued her husband at the age of 42.”
The relations here are “predicate”, “age”,
and “marriage status”.

Semantic Networks
Arch Types:
X is a Y. => Individual to Concept => X is a
tiger.
X is a kind of Y. => Concept to Concept =>
X is a wild kind of animal.
X is related to Y. => Individual to Individual
=> X Tiger leads the pack of Y Tigers.

Semantic Networks
Reification:
Relationships can be turned into a frame.
A frame can turn non-binary relationships
into an object.
A giver
A recipient
An object
Learn, Semantic Role Labels.

Semantic Networks
Multiple Inheritance

Semantic Networks
Multiple Inheritance
- Range Queries/Range Values

Semantic Networks
Intersection Search

Semantic Networks
Complex Semantic Networks

Semantic Networks
Inferential Distance

Semantic Networks
Partial Ordering – How the semantic
network should be shaped?

Semantic Networks
An example: From Hyper-structured to
Structured Data:

Semantic Networks
An example: From Hyper-structured to
Structured Data:
Learn, Semantic Similarity.

Semantic Networks
An example: From Hyper-structured to Structured Data:

Semantic Networks
Unified Medical Language System is an Example of Semantic Network.

Semantic Networks
A website can make the semantic network of the search engine richer and more accurate.
It can be the authority of truth, not just the topic.

Semantic Networks
An example of Semantic Network Visualization.

Semantic Networks
An example of Context Vectors.

Semantic Networks
Frames: Representation of Stereotypes.
Frames have inheritance.
Frames can have different data structures.
Frames are the typical semantic networks
for the same types of things.
Frames are useful for training AI.
At the right, you see a Frame.
Every class, and sub-class have specific
attributes.
All the relation types are clear.
In this case, Knowledge Bases are more
similar to the Frames.
Frames have “slots”, and “filters to be filled
in.
Values can be static knowledge.
A “daemon” can exceed the threshold and
make the knowledge “dynamic”.

Semantic Networks
Semantic Network Frame

Semantic Networks
Every frame slot can have a slot.
Every slot can have a “value”, “default”, “range”, “if-
added”, and “if-needed” property.
Frames help an SEO to create specific types of
document templates for specific types of query
templates.
Facets can include different types of semantic
networks.
• Represents the “default”.
• Every facet here can be entirely different semantic
network.
• A central entity will reflect the core of the specific
entity’s identity.

Semantic Networks
• Situational Frame: Valid only for a
situation.
• Action Frame: Actions for the situation.
• Combination Frame: Combination of
situational and action frames.
• General Frame: Hierarchy included
class models.
• These frames are for typical situations,
the unique or rare entities might not be
put into a frame.
• At the right side, you will see another
frame, but it is only for the restaurants.
• In the next slide, you will see an
example of FrameNet.

Semantic Networks
• “Jack went to a restaurant. He decided to order steak. He sat there and
waited for a long time. Finally, he got angry and left.
• “What was Jack waiting for?”
• “Why did he get angry?”
• A Frame Script can shape a knowledge base for different situations.
• Same script can extract all the information for the similar situations.
• Remember the “Restaurant” sample from Ramanathan V. Guha.

Semantic Networks
• Concept Maps: To create a Semantic
Network, a concept map is necessary.
• Every existence is connected to at least one
concept.
• “Eagle” can be connected to “flying”, or
“hunting” concept.

Semantic Networks
• Topic Maps: It sounds similar?
• It reflects any kind of concept, existing
things, or hypergraphs for associations,
occurrences and real-world things.
• A topic map connects concepts to the each
other along with the real world entities. A
topic map can be extracted from a content
network, and turned into a semantic
network.
• This brings us to the “Concept Graphs”.
• A concept graph is again for patterns.
• At the right side, you will see an example of
concept graph.

Semantic Networks
• A FrameNet is different than a frame.
• A FrameNet includes the semantic role labels but
in the form of more detailed patterns.
• For example, the sentence “The Unit found two
men hiding in the trunk of a car…” includes a
“sought entity”, “perceiver” and “location”.
• If we use the sentence like “The Unit found three
thiefs that are hiding in the storage”, relations are
same.
• It is similar to the “Frame Scripts”.
• But, FrameNet is actually a programming output to
help machines to read the semantic knowledge.

• Semantic Content Networks are the next
steps that come after the Topical Map.
• A Semantic Content Networks contains a
Context Vector, Context Hierarchy, Structure
and Connection.
• A Semantic Content Network is the textual
and visual form of a semantic network.
• A semantic content network can help a search
engine to change the truth in the knowledge
base.
• A search engine shapes the SERPs based on
the knowledge-base.
• A semantic content network can have a high-
level of knowledge-based trust, and search
engine can start to shape the SERPs network
based on the specific source.
• Semantic content networks can’t be imitated.
• A semantic content network can be beaten by
another one.
• A better context vector, hierarchy, connection,
and structure are must.

Semantic Content Network Results

https://www.oncrawl.com/technical-seo/creating-semantic-
content-networks-with-query-document-templates-case-study/

https://www.holisticseo.digital/theoretical-seo/ranking

https://www.authoritas.com/blog/encazip-case-study
Aggregator
Energy Industry
Encazip.com

Book Store
Huge Success
5th Largest E-commerce store
Of Turkey
BKMKitap.com

Blackhat + Semantic SEO
Expired Domain
Drinks

Dog-food Industry
Affiliate

Monitor Industry
Affiliate

Kanban Industry
Trained and SCN Designed

Blackhat + Semantic SEO
Expired Domain

SaSS
It was hard to train
these people.

Re-ranking Triggered
Dog-food Industry

Gift Industry
New Launch
Brand-new Domain
3th week

Exact Match Domain
Car Industry
7th week

Affiliate
Aggregator

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