What is Semantic Search?

The way search engines understand us, the users, has evolved: their interpretation of our searches is now deeply rooted in meaning, not just matching words.

From the basics to the advanced, here’s how semantic search works:

Semantic Search Technology Explained

Semantic search is a data-searching technique that strives to understand the meaning and intent behind what you type or speak into your device, rather than just looking for exact matches of your words.

Why did this come about? The development of semantic search was driven by the limitations of traditional keyword search, which often missed relevant information by focusing solely on literal matches.

Under the hood, semantic search is driven by natural language processing (NLP), enabling computers to understand human language, knowledge representation, often in the form of vast knowledge graphs that map relationships between concepts, and machine learning (ML), all working under the umbrella of artificial intelligence (AI).

Together, this technology combines to grasp what you, as a user, really want to find, focusing on the meaning of the words you use and the intricate relationships between the concepts they represent.

Think of it as a fundamental shift from simply matching strings of text to actually understanding the context of your query. For instance, if you search for “apple,” semantic search can understand if you mean the fruit or the tech company based on the surrounding words and your search history.

Learn more: Semantic search vs. keyword search.

How Semantic Search Works

Let’s move from the “what” to the “how”:

Natural Language Processing Foundations

At the heart of semantic search lies NLP, the branch of AI dedicated to enabling computers to understand and process human language. NLP is the first step, allowing search engines to analyze both the user’s search query and the content of web pages to decipher their structure and meaning.

Think of it like a linguist dissecting sentences. NLP techniques break down text into smaller units called tokens, identify the base form of words (stemming and lemmatization), and determine the grammatical role of each word (part-of-speech tagging, like is it a noun, verb, adjective, etc.?).

Consider the query “What are the best ways to bake a cake quickly?”. NLP analyzes this to understand that “ways” is a noun or adverb, “bake” is a verb, “cake” is a noun (the object), and “quickly” is an adverb modifying “bake.” It’s this grammatical understanding that helps search engines move beyond word strings and understand broader context.

Entity Recognition and Knowledge Graphs

Semantic search goes further by identifying key entities within text. Entities are real-world objects or concepts. Things like people (e.g., “Adam Sandler”), places (e.g., “Paris”), things (e.g., “electric cars”), and abstract concepts (e.g., “artificial intelligence”).

Once entities are recognized, search engines often lean on vast knowledge graphs, like Google’s Knowledge Graph. These are essentially huge interconnected databases that store information about these entities and the relationships between them. For example, the Knowledge Graph knows that “Adam Sandler” is the “actor in” “Happy Gilmore,” that “Happy Gilmore” is a “comedy” about “golf,” and that “golf” is a type of “sport.”

Vector Embeddings and Neural Networks

Another powerful technique in semantic search involves representing words and concepts as numerical vectors in a high-dimensional space. That might sound technical, but the core idea is that words with similar meanings are located closer to each other in this “semantic space.”

These vector embeddings allow search engines to find connections between words and concepts even if they don’t share exact keywords. Words that are used in similar contexts and have related meanings are clustered together. “Car,” “automobile,” and “vehicle” would form a tight cluster, while “banana” would be far away.

Creating these sophisticated vector embeddings often involves neural networks, particularly transformer models, which are trained on massive amounts of text data to learn these intricate semantic relationships.

Context and Intent Understanding

Finally, semantic search uses a variety of signals to understand the context of a query and, most importantly, the underlying intent. This goes beyond the literal words we use.

Search engines analyze the surrounding words in a query, a user’s past search history, current location, the time of day, and even broader search trends to get a clearer picture of what they’re really trying to achieve.

For example, if you search for “apple,” and you’ve recently been browsing tech websites and searching for iPhone reviews, the search engine is more likely to understand you’re interested in Apple, the tech company.

However, if you often search for recipes and healthy snacks, it might lean towards results about the fruit. It’s this contextual understanding that allows semantic search to provide far more relevant results for users.

The Impact of Semantic Search on SEO

What is the impact of the move toward semantic search on SEO? Well, content is now evaluated not just on the presence of keywords, but on its ability to comprehensively address user intent and provide meaningful information within a specific context.

What’s more,  tactics that once aimed to game rankings by excessively repeating keywords now not only fail but can harm a website’s visibility. That’s because semantic search algorithms are adept at recognizing unnatural language patterns and prioritizing content that is written for humans, incorporating relevant terms naturally within a broader topical context.

The focus has shifted from simply including keywords to demonstrating a deep understanding of the subject matter and providing genuine value to the reader. And as it should. That’s what search engine users really want: content that answers their questions and solves their problems.

Here are a few quick tips on how to do just that:

1. Instead of creating multiple pages targeting slight variations of the same keyword, develop comprehensive content that covers a topic in depth, addressing the various facets of user intent associated with it.
2. Aim to be the definitive resource on a given topic. Anticipate related questions users might have and address them within your content. The goal is to satisfy the user’s information needs as quickly as possible.
3. Write in a natural, conversational style that resonates with human readers. Incorporate synonyms, related concepts, and supporting vocabulary to demonstrate a thorough understanding of the topic.
4. Help search engines understand the meaning and relationships within your content by using schema markup. It provides explicit clues about the entities and their attributes on your pages.
5. Establish your website as a go-to resource for specific topics by creating a cluster of interconnected, high-quality content. That way, you’ll signal to search engines that you have a deep understanding of the subject matter.

7 Key Benefits of Semantic Search for Users and Businesses

The evolution towards semantic search has brought significant advantages to both those seeking information and the businesses striving to provide it:

1. A search engine’s ability to understand the meaning behind words allows it to deliver results that are far more relevant and precise.
2. When a user asks nuanced or multi-part questions, semantic search excels.
3. With the rise of voice assistants, we’re increasingly talking to our devices. Semantic search plays a big role here, as it can interpret natural, conversational language, not just short keywords.
4. The convenience of mobile and voice search hinges on quick, accurate results. Semantic search powers this by understanding the context of a user’s location (for local searches on mobile) and the natural flow of spoken language (for voice queries), making these experiences much more seamless and efficient.
5. Finding the right product online can be challenging with just keywords. Semantic search in e-commerce allows users to use more natural language descriptions.
6. For platforms suggesting articles, videos, or other content, semantic search analyzes the meaning of the content and user interests. This leads to more relevant and engaging recommendations, keeping users on the platform longer and increasing their satisfaction.
7. When users get irrelevant or frustrating search results, they’re likely to give up. On the other hand, providing more accurate and contextually appropriate answers from the first try, semantic search keeps users engaged, reduces bounce rates on websites, and leads to a more satisfying and productive online experience.

Conclusion and Next Steps

What’s next? Keep an eye on the advancements in NLP and knowledge graphs.

Experiment with how you formulate your own searches. You might be surprised at how well the machines are starting to understand.

And if you’re building anything online, start thinking less about isolated keywords and more about the rich tapestry of meaning that connects your content to your audience’s needs.

The semantic web is here, and it’s only going to get more… well, meaningful.