Entity SEO is an advanced approach to SEO concerning both on-page and off-page optimization activities. This approach considers not only the keywords, but also the entities or subtopics that constitute the page‘s topic. It allows for better targeting of content creation, keyword research, backlinks and social media outreach.

1. NLP for entity-based
and semantic SEO
MAX GERACI

2. Table of content
What is a Knowledge
Graph
What are structured data, and
what are they used for?
Subject-predicate-object
Semantic Publishing
Finding entities and
obtaining Topical Authority
Entity-Linking and
Wikification
Main benefits of creating your
Knowledge Graph
Topic Modeling and Content
Modeling
Entities for site
structure
Entity SEO implementation:
some results
01 02 03
04 05 06
07 08 09
10

3. Entity SEO is an advanced approach to SEO concerning both on-
page and off-page optimization activities. Following the
semantic evolution of search engines (from Lexical to Semantic
Search Engine), Entity SEO considers not the keywords but the
entities (or sub-topics) that constitute the page's topic.
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 entities.
In general, entities are objects or concepts that can be uniquely
identified, often people, places, brands, and “things”, in fact.

4. What is a Knowledge Graph
“A knowledge graph describes objects of interest and
connections between them.”
(Natasha Noy et al. Industry-Scale Knowledge Graphs:
Lessons and Challenges).
Specifically, this paper states that:
“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.”

5. Structurally speaking, a Knowledge Graph is s Knowledge base made
of Nodes and Edges (sometimes called Arches).
Nodes are entities, and Edges are the relationships between those
entities.
Each Entity is stored as a so-called “triple” consisting of Subject-
predicate-object.
We can also look at it another way:
Entity-relation-entity.
Subject-predicate-object

6. Main benefits of creating your
Knowledge Graph
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 (including internal search) 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.

7. Structured data are this metadata added to HTML. They can be expressed using different vocabularies
(Ontologies, to be more precise) and markup languages, schema.org and JSON-LD being the most used.
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.
Implementing unique Structured Data is the best way to make it explicit to Search Algorithms:
● The structure of a WebPage, i.e., the discrete units of content on it;
● the relationships among these various discrete units of content on the page as well as among the
site as a whole;
● The topics covered, i.e., the entities that contribute to defining it.
What are structured data, and
what are they used for?

8. Therefore, structured data act on two levels that concern:
1. the structure of the whole site, its pages, individual blocks, or discrete units of content on
the page. This will then involve describing whether the page contains an Article, a BlogPost, a
list (ListItem), a product feed (ProductCollection), or even blocks such as the Breadcrumb, a
video (VideoObject), a picture (ImageObjec), a how-to section (HowTo), or an accordion with
FAQs (FaqPage). Not only are these elements all defined through schema markup, but the
relationships and hierarchy between them are also defined so that we can say that a page
isPartOf a Website and that the video featured in a Article is the main entity of that page
(mainEntityOfPage). In addition, these discrete units of content are related to the Person,
Organization, LocalBusiness that performed or published them:
2. the second level of information we communicate to search engines through structured data
concerns the actual content and its meaning.
What are structured data, and
what are they used for?

9. Semantic Publishing
Semantic Publishing is 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 helps search engines,
voice assistants, or other intelligent agents
understand the page's meaning.

10. Semantic Publishing
Many SEOs struggle to understand what exactly an
entity is and tend to use entities like simple words,
and their synonyms. According to the definition I
have given, an entity is much more, i.e., the
conceptual understanding of a thing and its
relationships to other things.
This is what we as SEOs need to focus on:
rebuilding this network of semantic relationships
within our pages and our site.
I want to be totally clear here, Keywords do not
disappear, they are the strings to express entities.

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

12. Finding entities #1
● Google trends related topics is a precious and
underutilized resource;
● Google Suggest is now mainly suggesting
repeated topics;
● Wikipedia related topics;

13. Finding entities #2
● Wikidata SPARQL via https://query.wikidata.org/
#defaultView:Graph
SELECT DISTINCT ?item1 ?item1Label ?item2 ?item2label WHERE {
{
SELECT ?item1 ?item2 WHERE {
SERVICE gas:service {
gas:program gas:gasClass
"com.bigdata.rdf.graph.analytics.BFS";
gas:in wd:Q54837;
gas:traversalDirection "Forward";
gas:out ?item1;
gas:out1 ?depth1;
gas:out2 ?item2;
gas:linkType wdt:P279.
}
}
}
SERVICE wikibase:label { bd:serviceParam wikibase:language "en,da,sv,jp,zh,ru,fr,de". }
}

14. Finding entities #3
● the free tool https://www.entitree.com/

15. Finding entities #4
● Using a commercial tool like InLinks or that
draws topics maps starting from a top topic..

16. Finding entities and obtaining
Topical Authority
Another way to identify the entities in the content that
Google ranks for a query is to use Natural Language
Processing (NLP) models trained to recognize these
entities by processing a text.
As in the case of traditional keyword research, to identify
entities the starting point is competition analysis. Which
entities do they include in their content? Which
attributes? There are niche-dependant patterns that you
will start to recognize over time.

17. Finding entities and obtaining
Topical Authority
The less "obvious" entities emerging from this analysis are
useful not only in defining our topic and gaining Topical
Authority.
You can collect these entities using The Entities’ Swiss Knife
https://entitieschecker.com
Providing information that is not obvious and otherwise more
difficult for our users to find is the best way to show Google
how you produce "useful content" with a real informative value
and not just another “commodity content” which is yet another
copy of what is on the Internet.

18. Entity-Linking is the process of identifying entities in a document and relating these entities to their
unique identifiers in a Knowledge Base. Wikification occurs when entities in the document are mapped to
entities in Wikimedia Foundation resources, namely Wikipedia and Wikidata.
The schema vocabulary properties used for Semantic Publishing -that bridge between Structured Data and
Entities- are the about, mentions, sameAs, and knowsAbout.
Entity-Linking and Wikification

19. Entities for site structure
Building a proper site structure is an important step if
you want Google to understand your content well.
There is nothing better than structuring your website
using the power of Semantic Publishing and
presenting it as a Knowledge Graph connected to
trustable public Knowledge bases.
If you are using a siloed structure, a Silo head has its
main Topics/Entities declared as about properties
and the sub-topics as its mentions. These sub-topics
are fully developed in supporting articles, each one
focused on (about property) a single sub-topic
“mentioned” in the Silo head.

20. This Ahrefs report shows a confrontation
of some ranked keywords between October
and August when Entities were injected in
the structured data at a category level
(using the same about property in all the
articles of each category).
Entity SEO
implementation:
some results

21. What happened to Discover after injecting entities in the Organization and Person (for the
authors) schema as knowsAbout properties:
Entity SEO implementation: some results
After the September Google Helpful content update, the site gained the authority to be shown in
Discover, even for articles that were there for many months.

22. THANKS FOR
WATCHING