Entity SEO is an advanced approach to SEO concerning on-page optimization. Following the semantic evolution of search engines from Lexical to Semantic, Entity SEO considers not the keywords but the entities (topics or sub-topics) that constitute the page's topic.

1. Max Geraci
Entity-based SEO:
Semantic Publishing and Entities
Gap Analysis

3. Entity SEO
The article “Introducing the Knowledge
Graph: things, not strings” published in the
official Google Blog in 2012 is the watershed
marking the birth of Entity SEO.
The “strings” in the title are the sequences
of characters that make up keywords, to
understand and simplify, we can say that
“things” is more or less a synonym for entity.
In general, entities are objects or concepts
that can be uniquely identified, often people,
places, brands, and “things,” in fact.

4. Resources
Here is a short list of resources for further study.
Wordlift and Ontotext blogs:
https://wordlift.io/blog/en/
https://www.ontotext.com/blog/
Some academic texts:
- Guarino et al., Ontologies and knowledge bases:
towards a terminological clarification
- Hogan et al., Knowledge Graphs -
https://arxiv.org/pdf/2003.02320.pdf
- Noy et al. Industrial-scale Knowledge Graphs
- Norvig e Russell, Artificial Intelligence a Modern
Approach

5. Ontologies and
Knowledge Graphs
Ontologies are semantic data models that define the
types of things that exist in a knowledge domain and
the properties that can be used to describe them.
There are three main components to an ontology:
Classes Relationships Attributes
The distinct
types of things
that exist in our
data
Properties that
connect two
classes
Properties that
describe an
individual class

6. Ontologies serve to create a formal representation of the entities in a
Knowledge Graph, being its backbone.
A knowledge graph is an “instantiation” of an ontology. In other words:
Ontologies and
Knowledge Graphs

7. Knowledge Bases and
Knowledge Graphs
A Knowledge Bases (KB) as a set of
sentences/facts expressed in propositional logic.
An essential point is that from a KB, you can use
inference techniques (or knowledge reasoning) to
derive new knowledge from this knowledge base.
We can view a Knowledge Graph as a
Graph database or a so-called RDF
Triplestore, which visualizes a KB, or a
graph-structured knowledge base.

8. RDF Triples
Each triple consists of three parts
Subject-predicate-object
Node 1
for the subject
Node 2
for the object
We can also look at it another way:
Entity-attribute-value,
or again,
Entity-relation-entity
an edge for the predicate that goes
from a subject to an object

9. So, here we have our new definition of KG,
which is as brief and straightforward as
possible:
A knowledge graph is a graph
representation of an “instantiation” of an
ontology
A “graph” representation is made of nodes and
edges, organized into triples following the RDF
standard.
Nodes are entities, and edges are the
relationships between those entities

10. Knowledge Reasoning and
Knowledge Graph Completion
Just as with Knowledge Bases, in the context of KGs,
we also have a task/problem similar to inference (or
knowledge reasoning), known as graph completion.
Knowledge graph completion is the act of inferring
new edges, entities/things/facts based on the already
existing relational data.

11. Industry-Scale
Knowledge Graphs
In terms of enterprise knowledge graphs, an essential reference is the paper by
Natasha Noy et al. Industry-Scale Knowledge Graphs: Lessons and
Challenges
“a knowledge graph describes objects of interest and connections between them.
[…] Knowledge graphs provide a shared substrate of knowledge within an
organization, allowing different products and applications to use similar
vocabulary and to reuse definitions and descriptions that others create.”

12. Knowledge graphs, represented in standardized
and interoperable RDF triples, provide the best
framework for data integration, unification,
linking, and reuse.
A KG is a real asset through which the
information conveyed by one of our sites is
immediately accessible to search engines and
“intelligent agents” such as conversational agents
or recommendation engines for related content or
products in e-commerce.
So, the main benefits of creating a KG of your
site are Improved Findability, Greater Content
Grouping and Reuse, and Improved SEO.

13. Semantic publishing and
Entity-Linking
Semantic Publishing is the activity of publishing a page on the
Internet by adding a semantic layer (i.e. semantic enrichment) in
the form of structured data that describes the page itself.
Semantic Publishing relies on adopting structured data and
linking the entities covered in a document to the same entities in
various public databases.
Entity Linking is the process of identifying entities in a text
document and linking these entities to their unique identifiers in a
Knowledge Base.

14. What are structured data, and
what are they used for?
Structured data are this metadata added to HTML that make explicit in a
way that is immediately understandable by machines:
The topic covered, i.e.,
the entities that
contribute to defining it
The relationships among
the various discrete units
of content on the page as
well as on the site
The structure of a page,
the “discrete units of
content” on it (video, FAQs
accordions, a product
feed.)

15. Topic Modeling and Content
Modeling
The mapping of discrete units of content—Content
Modeling—can be usefully carried out in the design
phase, especially today when we tend to design by
blocks.
The content model can be related to:
the map of topics we cover or
will cover on our website
(Topic Modeling)
the structured data
through which it is made
explicit.

16. Schema.org properties: about,
mentions, sameAs, @id
The schema vocabulary properties used for Semantic
Publishing and that bridge between structured data and Entity
SEO are the about, mentions, knowsAbout, and sameAs
properties.

17. Thanks For Watching