As search engines continue to evolve, so do the strategies for optimizing websites to improve rankings. One of the latest and most popular terms is Semantic SEO. As an SEO agency in Indonesia, we also adopt a semantic-based strategy in our services.

In this article, we will explore the world of Semantic SEO techniques—a mindset that focuses on optimization based on artificial intelligence (AI) and machine learning. Get ready to discover how this revolutionary approach is transforming the way we understand and interact with search engines to bring your website to a higher level of visibility and success. Let’s begin our exploration by understanding what Semantic SEO and Semantic Search Engines are.

What is Semantic SEO and Semantic Search Engines?

Semantic SEO is a search engine optimization strategy that focuses on creating a network of relevant and meaningful content to form a topic and the information needed by search engine users. This means that instead of focusing on specific keywords, Semantic SEO focuses more on meaning, entities, topics, and becoming a trusted source (authority) for Google and users on a specific subject.

Google itself is a semantic-based search engine that establishes relationships between entities, search intents, and organizes information on the web. Thus, the structured content with interconnected entities forming a concept (Information Extraction) becomes crucial for search engines to meet user information needs (Information Retrieval).

Some Key Concepts of Semantic SEO You Need to Know

The concept of Semantic SEO does not replace traditional optimization methods that have been practiced over time. Common content optimization, such as Technical SEO Audits, On-Page, and Off-Page Optimization, are still performed.
Semantic SEO encourages you to think like an engineer rather than a content writer, as you will learn more about how machines/algorithms process information.
Semantic SEO is not about learning Google’s patents or cracking its algorithm.

In an interview with Gary Illyes, a member of Google’s Advocacy Engineer Team, he mentioned that “only a small percentage of Google’s overall algorithm is a black box and cannot be published. However, the majority is the Information Retrieval System, which is available publicly.”
  https://www.youtube.com/watch?v=lAthItDEL3A

In our exploration, we also found that developments in how machines understand human language, as seen with ChatGPT or Google Bard, are also results of Google’s publicly shared research (read more here: Google Research Pub).

Thus, while Semantic SEO is an advanced SEO concept, it is adopted by Google and can be mastered by many practitioners.

Jargon and Terminology in Semantic SEO

Before diving deeper into this article, it’s good to familiarize yourself with some important terms within the Semantic SEO concept, so that when reading further, you won’t lose context.

Search Intent

Search Intent refers to the goal and purpose of a user when conducting a search in a search engine. There are four types of Search Intent:

• Informational: Users are looking for information about something, such as how to do something, answers to questions, or summaries of certain topics.
• Navigational: Users are searching for a specific website or web page, such as a company website or product page.
• Transactional: Users are looking to buy something, such as products, services, or tickets.
• Commercial: Users are browsing for services, products, or brands.

Central Search Intent refers to the main goal of a user during a specific search session.

For example: If you’re reading this article, the Search Intent would be to obtain information about Semantic SEO (Informational), but your main goal is to engage in digital marketing (Central Search Intent).

Understanding Search Intent and Central Search Intent is vital for SEO because it helps you create content that is relevant and attractive to users. When you understand what users are searching for, you can create content that is more likely to appear in search results and attract users seeking the information they need on your website.

Read Also: Understanding Search Intent: A Fundamental in SEO Strategy

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a computer process that allows machines to read content and understand its context. This helps machines understand the relevance of content to user queries. NLP also enables machines to identify the core theme of content and helps improve Semantic SEO relevance. The processes within NLP are too technical to explain here as they involve concepts like Tokenization, Stemming, Vector, Embedding, and others. We’ll cover this in a future article.

Information Retrieval

Information Retrieval in search engines is the process of finding relevant documents or other data sources from a data set (Big Data) that matches user requests and intents. In one of Google’s patent documents, to determine a user’s primary intent (Central Search Intent), Google uses information from keyword profiles and user activity in search engines.

Information Extraction (IE)

Information Extraction is the automated process of extracting structured information from unstructured and/or semi-structured documents or machine-readable sources. IE is used in various applications, including:

• Libraries and Information Retrieval: IE can be used to extract metadata from documents, such as titles, authors, and abstracts, to help users find the information they’re looking for.
• Natural Language Processing: IE can be used to extract information from text documents, such as names, dates, and locations, to help NLP applications understand the text.
• Business Analysis: IE can be used to extract information from financial reports, contracts, and other documents to help companies make better business decisions.

There are two main approaches to IE: rule-based and statistical. Rule-based approaches use a set of rules to determine what information should be extracted from documents. Statistical approaches use statistical models to determine what information is likely to be present in documents.

IE is an active research field with many challenges, such as ambiguity in language, where words and phrases may have different meanings in different contexts. Another challenge is scalability, as IE can become expensive to implement on large datasets.

Despite these challenges, IE is an important research field with many potential applications. It helps us understand information in unstructured and semi-structured documents and aids decision-making.

Entities

In NLP, an entity is a unit of information within text. Anything that can be defined is an Entity.
Entities can refer to names of people, places, organizations, products, dates, or times. Entities can be identified using techniques like tokenization, POS tagging, and named-entity recognition.

There are various types of entities, including:

• Person: Refers to people, like “Viktor Iwan” or “Ratna”.
• Place: Refers to places, like “Indonesia” or “Jakarta”.
• Organization: Refers to organizations, like “Google” or “Doxadigital”.
• Product: Refers to products, like “Android” or “Toyota”.
• Date: Refers to dates, like “2023-07-28” or “1776-07-04”.
• Time: Refers to times, like “10:00 AM” or “6:00 PM”.

Entities are a crucial component of NLP and are used for various tasks. Understanding entities helps to comprehend text better and derive insights about the world.

Lexical Relation and Entity Attribute

Lexical relations refer to relationships between two words or phrases that are related in meaning. These relationships can be synonyms, antonyms, hyponyms, or hypernyms.

Entity attributes refer to the properties of an entity, such as a person’s name, age, gender, or occupation.

For example:

• Lexical Relation:
  • Synonym: big – large
  • Antonym: big – small
  • Hyponym: cat – animal
  • Hypernym: animal – cat
• Entity Attribute for Person (e.g., John Smith):
  • Name: John Smith
  • Age: 30 years
  • Gender: Male
  • Occupation: Engineer

Lexical relations and entity attributes are important in NLP and can be used for tasks such as:

• Understanding text meanings
• Searching for information within text
• Translating languages
• Writing creative text
• Answering questions

Evolution of SEO: From Traditional SEO to Semantic SEO

Google has consistently pursued its goal of becoming the leading search engine by implementing various innovations that have revolutionized the way websites are ranked and optimized for personalized search results.

• 2010: One of the most important milestones in Google’s evolution into a semantic-based search engine was the acquisition of Freebase, a semantic database that provided structured entity data.
• 2012: This acquisition enabled Google to enhance its Knowledge Graph, a database that organizes information in a way that creates knowledge from available data. With the Knowledge Graph, entities are linked through relationships, attributes, and thematic contexts, enabling a deeper understanding of the information presented in search results.
• 2013: Google introduced the Hummingbird update, marking a significant advancement in its ranking algorithm. This update aimed to better interpret complex search queries and understand the true intent behind user searches, improving search result relevance by matching them more accurately to users’ desired information.
• 2014: A key innovation introduced was the Knowledge Vault, which allowed Google to automate data mining from unstructured sources and extract valuable information, expanding its Knowledge Base. This laid the foundation for future advancements in Natural Language Processing (NLP), enhancing its ability to interpret search queries.
• 2014: Google’s commitment to semantic search and its correlation with website quality evaluations became evident with the implementation of the E-A-T (Expertise, Authority, Trustworthiness) ranking system, emphasizing the evaluation of expertise and authority of content creators.
• 2015: Google integrated Machine Learning into its search algorithm with RankBrain, using vector space analysis to better understand relationships, thematic proximity, and contextual relevance of search queries, which improved intent interpretation.
• 2018: Google introduced BERT, a technology that uses Natural Language Processing to gain deeper semantic understanding of search queries, sentences, and content, allowing Google to understand language nuances and context, improving search result relevance.
• 2020-2023: Google continues to push for improving its semantic capabilities with innovations like MUM (Multitask Unified Model), which improves complex search result understanding and multi-modal search.
• 2023: Google’s Search Generative Experience (SGE) enhances the search engine’s ability to deliver more personalized results with conversational AI, presenting users with answers that have more context and relevance than ever before.

This SEO process goes hand in hand with the evolution of NLP and AI, gradually redefining how search engines understand content, its context, and its relevance to the search query, rather than focusing solely on keywords.

By adopting Semantic SEO strategies, webmasters and content creators can ensure that their websites are optimized for these advanced algorithms, making their content more discoverable, relevant, and ultimately better ranked.

Feel free to consult with the Doxadigital team for insightful and optimized ideas in implementing semantic SEO strategies for your business. Contact us via email at info@doxadigital.com or chat with us on WhatsApp at +6281288883692.

Viktor Iwan( CEO and Founder of Doxadigital Creative Digital Marketing Agency )

Viktor Iwan adalah CEO dan pendiri Doxadigital Creative Digital Agency. Dia juga merupakan pembicara dan pelatih publik dalam berbagai acara pemasaran digital seperti “Social Media Week”, “Tech in Asia”, “WordCamp”, “SEOCon”, “QuBisa Bootcamp”, dan “Google Agency Bootcamp”. Viktor Iwan memiliki sertifikasi Google Ads, Facebook Lead Trainer, Facebook Media Buying and Planning, dan Google Analytics. Dia juga menjadi salah satu dari 5 Product Expert Google Ads asal Indonesia oleh Google Inc. Viktor Iwan juga memiliki website pribadi di viktoriwan.com.