What is Semantic Web?

Semantic Web is the vision of converting data on the internet into something interpretable and understandable by machines.

In simple words, a semantic Web refers to a Web of content where machines can process the meaning.

According to Wikipedia, the goal of the Semantic Web is to make Internet data machine-readable.

But what does this mean—and why is it so powerful?

To understand the meaning of Semantic Web, let's have a brief history lesson on 'information,' specifically how connections are formed between pieces of information on the internet.

Before the internet, scientists and researchers created documents (research papers, studies, books, etc.) and referenced other documents through 'citations.

As a reader of those documents, you'd have to follow the citations and probably visit the library to find the exact copy you wanted. You can imagine how time-consuming and tedious that was for people.

With the invention of Web 1.0, hyperlinking was born. A person reading a document could click a hyperlink (URL) to open a connected document. This has created a "chain of documents" and reduced the time to check resources and references.

For consumers of online content, the Internet cloud was terrific because it abstracted the whole activity of connecting information away from us. So, for example, instead of finding documents physically, you could give them an address and hyperlink them to other documents. This is how all documents are connected on the Web.

Web 2.0 was the era of mobile apps and social media platforms such as LinkedIn, Facebook, Twitter, Yelp, and other apps that you can interact with daily. This Web iteration is to store your data on the cloud instead of in a physical location—like your computer. However, despite how great this thing is, the Web data is still unstructured and disconnected.

Web 3.0 is to connect the data and make it machine-readable, not just the documents or Websites on an upper level but all the data on the lower level. This will allow for specific data to be referenced between documents. This concept is known by (linked data)

think of the Semantic Web as a kind of knowledge graph, and Google already has something they call a knowledge graph which is a sort of node and link diagram, so you have the nodes that represent the entities that can be a car or person and some links connecting them that can be labelled to describe the relationship between these entities so you may have the node that means one person and a husband link that points to another person, and we have a husband node that links to an address.

Having this kind of representation is good if you want to represent binary relations between two things, so if you have a three-word sentence, you can define that using this very kind of graph structure. So with the semantic Web technology, you can represent and reason with these entities in much more complicated expressions that go way beyond what 3 or 4 words sentences are. which is really what the semantic web can represent and what knowledge graph can represent as well.

The basic idea underlying Web 3.0 is that you can connect datasets instead of linking documents. The benefit of this arrangement is that you no longer have to think about specific documents. Instead, you only have to think about the data or information you need.

As Web 1.0 made it unnecessary to think about the physical location of information, semantic Web will make it unnecessary to think about specific documents. Instead, you'll only think about the information you're looking for.

In the semantic Web, search engines evaluate queries to understand the intent and context instead of just serving up content that matches the keywords used in the search query; that's why keyword techniques are getting less potent while adequately researched and written content is gaining more exposure on search engines

How Does the Semantic Web Work?

During Web1.0 and most Web2.0, all Search Engines were just “Lexical.” meaning they were checking whether a keyword in the user’s query was in a corpus. During that time, concepts such as the number of words and keyword density were still significant.

Nowadays, Search Engines give more importance to the meaning of entities, their context and the correlations between them.” Search Engines, such as Google, know the connection between keywords and have a Knowledge Base for it in Google Knowledge Graph, to better understand search intent.

Google’s semantic search Patent 1999

I want to express my appreciation to Bill Slawski for pointing out to me a google search patent called “extracting patterns and relations from scattered databases such as the world wide web,” which seems to be an early attempt for google to organize the data in a machine-readable form.

This image is from Sergey Brin’s Patent:

This patent aims to search big data by maximizing the coverage and minimizing the error rate. However, a low error rate is more critical than high coverage, giving a significantly extensive database (the World Wide Web). an important observation is that given a set of patterns, P with high range and low error rate, the algorithm can construct an excellent approximation for error rate and the search result with a high correlation with the searched keyword

Despite advances in Web technologies, most data online is designed to be read by humans, not machines. This makes it difficult for spiders (Web Crawlers) to analyze Web pages and give valuable results for search queries.

Semantic Technologies

The goal of semantic Web is to help machines understand data by adding either one of the following semantic technologies to the content

• JSON-LD: JavaScript Object Notation for Linked Data
• RDF: Resource Description Framework
• OWL: Web Ontology Language
• FOAF: An acronym for a friend of a friend
• Microdata

Adding this to your content gives search spiders more room to understand Web pages. In addition, this makes it easier for search engines to feature the most accurate results for a search query, in the SERPs. The Semantic Web relies on linked data, making it easier for us to publish site metadata. I will explain both semantic metadata and linked data in the following paragraphs.

Linked Data and Semantic Metadata

Semantic Metadata

Semantic Metadata data refers to the descriptive tags added to a Web page content. While invisible to human readers, metadata tags are visible to machines that use them to evaluate a Web page's contents.

Unlike HTML tags, semantic tags are created using machine-readable data formats such as JSON (Javascript Object Notation) and RDF - Semantic Web Standards. As a result, search spiders can extract information about Web pages and determine their relevance to search queries.

Marking up Website content with semantic metadata increases your visibility on search engines; crawlers can "read" the information and display it in relevant search results. Note that does not necessarily mean higher rank, but it is for sure more exposure.

Linked Data

Linked data is the mechanism through which Webmasters can create connections between entities, Linked data is the core principle of the Semantic Web concept.

Here is an example on How to use Interlinking for semantic SEO

If we want to transform the Web from a collection of documents of strings to a giant database, there must be a way to connect the information in a structured manner.

This need has fueled the decision of major search engines (Google, Yandex, Bing, and Yahoo!) to develop Schema.org in 2011. With 1482 entities in its library, schema.org allows Webmasters to add linked data to their sites and enhance their rich results. Linked data is added through one of the relevant properties of a schema type that can connect it to another entity.

Semantic Web Milestones

The semantic Web is still a work in progress, with several moves speeding up its development. Here is a summary of the Semantic Web's milestones:

• 1999: Google's First Semantic Search Invention
• 2001: Tim Berners-Lee introduces the Semantic Web

Tim Berners-Lee, the inventor of the World Wide Web, published an article in Scientific American outlining his vision for a Semantic Web. In this new version, information would be readily accessible to search spiders who would find it easier to match users to relevant results.

• 2011: Google collaborates with Bing, Yahoo, and Yandex to create Schema Org

Schema.org is a library that Webmasters could use to publish structured data on their Websites. Schema.org contains over 1,400 distinct entities and allows us to structure and link all the data that we have not just on our websites but also to everything in the healthcare industry, automotive industry and many more, all this data is transformed in a way that bots can understand. If you want to generate semantic data and don't know how to code, you can try Schemantra; you can create more than 1,400 different entities, but more importantly, it has a feature that allows you to link your data together and build knowledge graphs. It's for webmasters to optimize for semantic Web.

With Schemantra you can generate semantic metadata for your content. Let's take this article you're currently reading as an example. Instead of the usual HTML tags, we'd add descriptors like the Date of Publication, Author, Content-Type, and Word Count. There are even more descriptive tags that we can use for our content in the Schema.org library!

• 2012: Google releases Knowledge Graph

The Knowledge Graph database contains unique entities and properties and displays the relationships between them and other entities online. By now, search engines can distinguish between entities (People, Objects, Locations, etc.) and properties.

• 2013: Google releases the Hummingbird update
This is the first time Google has attempted to move away from keyword-matching to context-matching. The Hummingbird algorithm evaluates search queries using Natural Language Processing (NLP) to understand searcher intent and context. With the Hummingbird update, users may get results in the SERPs, even if those documents fail to include the keywords used in the queries. The focus here is on topics, not keywords.
• 2015: Google releases the RankBrain update

RankBrain is built on top of the Hummingbird algorithm and extends the power of algorithms to understand the meaning of a search query. In addition, RankBrain contains a significant feature — it uses machine learning to process search queries.

The RankBrain algorithm continuously learns what results find useful and updates its library. By understanding what users want when they use particular terms, the algorithm can return the best results in the future.

• 2019: Google releases the BERT update

BERT means 'Bidirectional Encoder Representations from Transformers. Like previous updates, BERT represents another advancement in Semantic Web development.

Google's search engines can understand complex, long-tail queries better than ever with BERT. As a result, content publishers can target those long phrases that searchers use in queries.

Why Is Semantic Web Important for SEO?

So, why is the semantic Web important — especially from an SEO perspective? Here are some reasons Google is pushing for a move to semantic search:

• To make data machine-readable

The Semantic Web is an improved version of the World Wide Web that uses machine-readable data to facilitate faster processing and retrieval of information.

Despite advances in Web technologiese, most data online is designed to be read by humans, not machines. This makes it difficult for spiders (bots) to analyze Web pages and give valuable results for search queries. Currently, many sites use HTML text to describe the content of a Webpage. For example, you can use header tags to show spiders the various headings in an article.

But machines still cannot understand the context of a Webpage with HTML. For example, an H1 tag may show that a text is the first heading, but it says nothing about the page itself or its data.

The problem this creates is obvious: Because the content of these pages are not machine-readable, spiders cannot accurately predict their relevance to a search query. Even though search spiders can select pages through keyword matching, the process isn't entirely fool-proof.

The semantic Web is designed to enhance the traditional Web and allow machines to access, interpret, and share online information. This is done by using metadata to describe Web pages in a language that computer spiders can read.

• Semantic Web can handle ambiguity in search queries.

Sometimes, a user might use unintentionally confusing language in a search query. Search engines may find it difficult to find accurate results for such queries without semantic Web technologies.

Imagine if someone typed "Java" in the search field. The task here is to understand if the user wants results related to the programming language or the city (Java, India).

Semantic Web considers location, user search history, and global SERP position, amongst other factors, to judge the intent behind each query. Thus, the user will find results that are closest to their intent.

• Semantic Web provides better answers.

Have you tried asking a question on Google only to get a relevant answer? Semantic Web technologies ensure users get better responses to their queries.

With the linked data technology, the semantic Web uses ontologies, which allow information to be classified into "taxonomies" and "classes." An ontology can identify the class of an object; for example, a search engine can tell the difference between Paris, the actress (Person), and Paris, the city (Location).

Ontologies can also detect relationships between particular items. For example, machines can notice that "Toyota" and "Camry" have a relationship, where "Toyota" is the manufacturer of the Camry. These are just basic-level relationships; semantic Web technologies can form with the linked data. This leads to richer results since spiders will return only pages with related information, meaning more specific answers.

These ontologies are contained in Schema.org, which is the Internet's "dictionary." Schema.org allows search spiders to match queries to specific entities instead of keywords. This dictionary contains multiple categories and serves as a vocabulary for search engines—so they can understand search terms better.

How to Optimize Content for Semantic Web

One way to optimize your content for semantic Web is to optimize for entities and the relationship between them in the relevant contexts.

Nowadays, webmasters cannot get away with publishing low-quality, keyword-stuffed content and hope to rank for a semantic search engine.

To create relevant content for users, Here are five tips on how to perform SEO in the age of the semantic Web:

1. Build content around entities, not keywords

To optimize for semantic Web, build content around entities, not keywords Entities are a critical factor in the semantic Web. An entity is a thing with distinct and independent existence. Examples of an entity are a single person, product, service, article or a single organization.

When i asked Bill Slawski how to optimize for the semantic Web, he said: "find all appropriate entities to include on a page, show how they are connected. Then, build ontologies that show off their relationships". This definition sums up the entire semantic SEO process in one statement.

However, Keywords are still important in SEO and will not go away soon. Moreover, there is nothing against building content around keywords; it is up to the content creator to choose their strategy. Some people look at keywords for monetization, and others look at the content to build authority. So it is always up to the creator of the content. However, Google understands that similar keywords are different ways of expressing the same idea and ranks them similarly.

Instead of creating thin pages that target similar keywords, invest in creating in-depth articles that cover a topic thoroughly. It may take time, but the article will start ranking for different keywords and experience considerable search traffic.

2. Use Structured Data

To describe entities and their semantic relationships, Use Structured Data (Schema markups) Schema markups can describe entities and show the semantic relationship between them

here is an SEO case study i conducted about how to use structured data to increase your website traffic

and here is an article i wrote about how to optimize your structured data for semantic SEO

Schema.org was established in 2011 by Google, Bing, and Yahoo remains the most notable semantic tagging scheme available. Hundreds of terms describe discrete entities, from meal cooking times to movie characters. Moreover, using structured data is not only helpful for increasing the Website's visibility to search engines. Structured data are viewed as rich snippets, which increases the site's SERP performance and click-through rates.

When building schema markups, describe the Website content as they are, do not add things up if they do not exist on the Webpage, think about how schemas will be linked together and what connections are between the nodes and entities.

3. Optimize for user search Intent

Understanding user search intent is crucial in optimizing content for search engines. The semantic Web calls for creating content that helps users find accurate answers and satisfies intent.

Before creating articles, blog posts, or pillar pages, research the common queries that may bring a user to a Website. This will allow you to brainstorm topic ideas that reflect user intent and enable you to take advantage of semantic search. Looking for what people searched for about your topic is an excellent way to predict search intent. I use Answer the Public. It will show all the questions people ask around any keyword.

4. Use semantic topic structure for building content.

Building a semantic model for your website is the first building block on how to optimize your content for semantic SEO

Why Semantic Structures

Wait.. What?

Semantic structure is a fancy term for putting our meaning ("semantics") into sentences; an English sentence is a semantic structure. For example:

subject - verb - object Now you might think, ok, the words have meaning, but not a structure; then i would say: ok, read this: "A man rides a horse." "A horse rides a man."

Words have meaning, but two sentences containing the exact words in the same structure can have very different meanings - depending on the structure. So in the first case, it is the man who rides the horse, and in the second case, the horse who rides the man, which is also nonsense. Well, maybe sometimes. Changing the position of the words (or "terms") within the structure changes the meaning. Semantic structure is a deep topic. For more information, please check the linked document

5. Keywords are still relevant (semantically related keywords)

As described earlier, semantically related keywords are still crucial to SEO. Therefore, once a topic-cluster strategy is in place, start finding relevant phrases and long-tail keywords that can feature a Website content and answer the related questions for user search intent.

What are The Recommended Semantic SEO Tools?

One way to optimise for semantic SEO is to use semantic SEO tools; The semantic tools are required for webmasters to optimize content for user intent, define entities, describe the relationship between them and cover a topic with relevant content

Here are a few semantic SEO tools that can help for semantic SEO

1. Google NLP

Google NLP can find all the entities that google recognize in a piece of content, which is a key to optimizing for semantic SEO. I was shocked the other day when I asked webmasters on Twitter about their top two most recommended SEO tools. I got over 100 fantastic recommendations from the most incredible community in the world. However, none of them mentioned Google NLP. The powerful pre-trained models of the Natural Language API empower webmasters to easily apply natural language understanding (NLU) to their corpus with features including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis. You can copy and paste text and see what comes up. its one of the top semantic SEO tool on the web along with ChatGPT

The image above is from Google NLP API syntax Analysis

2. Schemantra: Schema Markup Generator

Many Schema Markup Generators will automatically create structured data for a website, but a few generators will allow leveraging the semantic relationships between entities To boost your SEO

I created this YouTube video to demonstrate Schemantra's full capabilities, such as creating schema markups, nesting, linking and building knowledge graphs for a car and an AutoDealer shop.

Schemantra is a semantic SEO tool that can generate more than 1,400 different JSON-LD schema types to describe any Website. Use Schemantra to define all your entities and describe the semantic relationship between them using JSON-LD.

What is unique about this tool is that it allows you to create relationships (context) between your markups to achieve maximum optimization for the semantic SEO, instead of having orphan sets of disconnected structured data all over your Website.

Schemantra can build JSON-LD knowledge graphs by connecting different schemas within the same page or Website.This is recommended for maximum optimization.

The image above is from Schemantra Navigator.

3. Also Asked

AlsoAsked is a semantic SEO tool that uses data to provide the most closely related questions to a user's query, which can help you improve your content and demonstrate to search engines that your page is likely the best possible result.

AlsoAsked is one of the few tools that provides search results in real-time, revealing what the public wants to know about right now.

AlsoAsked is an incredibly useful tool for generating content ideas and identifying significant gaps in content in a relational way that helps users visualize how topics fit into a larger content piece. It's one of the first research tools I turn to and recommend when starting any content-related project, regardless of its scale.

4. Answer the Public:

Understanding what users are looking for is critical in optimizing content for semantic search engines. This is a semantic SEO tool to get instant, raw search insights direct from the minds of users, use Answer the public to discover what people are asking about. By using Answer the public you can build the content structure to match the answers users are asking about

5. Keyword Planner and SERP API

Planning the right keywords with relevant context is essential for semantic SEO. Most webmasters are familiar with keyword planners, but not many are familiar with SERP API. With a few coding skills, SERP API can Scrape Google and other search engines from this fast, easy, and complete SERP API and get all the data required, not only about KW stats but also about anything in Google Knowledge Graph.

Conclusion

The Web is changing and adopting new technologies all the time. So the Semantic Web is the obvious next step for the internet. Luckily semantic SEO is an ongoing learning process, and we have to catch up with the ever-changing new technologies, learn the methods and the tools needed, and adapt to them.

To optimize for semantic Web, use Google NLP to build content around entities, not keywords. Use Schemantra to describe entities and their semantic relationships for search engines. Use "Answer the Public" and "AlsoAsked" to optimize for user search intent. and use Keyword Planner and SERP API to find the right keywords with relevant context

I hope this paper will show the way to all the enthusiasts out there looking for a basic introduction to understanding the Semantic Web and the Semantic SEO tools needed.