Semantic Search Explained

Once upon a time, internet search was primarily about matching keywords to content, in the literary sense. If the keyword or phrase you searched for wasn’t present on a web page or article, the page probably wouldn’t rank well in the search engine. 

But, as it turns out, keywords alone aren’t necessarily the best way to find relevant content. They are an important signal, but relying on them can be problematic. For one thing, people don’t always say (or type) what they mean. It’s also easier to game search algorithms when keywords are the main ranking signals, which can lead to poor quality content and even poorer user experiences.

Semantic search, an approach that looks beyond keywords to decipher searcher intent and context, is becoming an increasingly important way to improve digital experiences. Rather than simply matching keywords, semantic search aims to understand the meaning and context behind a user’s query. This allows search engines to deliver more relevant and accurate results, even if the exact keywords are not present on the page.

What is Semantic Search?

Semantic search uses two AI techniques – natural language processing (NLP) and machine learning to extract meaning from search queries. It leverages techniques like vector embeddings, which represent words and phrases as numerical vectors, and vector similarity to match the semantic meaning of a query to relevant content. 

Let’s say you’re searching for “black stilettos” on an ecommerce website. Your search comes back with nothing – zero results – even though you know the retailer sells many styles of high-heeled black shoes. With traditional keyword search, you’re getting zero results because the term “black stilettos” isn’t present in any of the retailer’s product descriptions. There are many ways to describe stilettoes, but the search engine doesn’t know that spiked heels, high-heeled party shoes, femme fatal shoes, needle heels, pin heels, etc. are all appropriate matches for your search.  

A semantic search engine understands that these different phrases all refer to the same type of footwear – shoes with long, thin, high heels. By matching the semantic meaning of the query to the content, the search engine is much more likely to produce relevant results – pumps, sandals, boots, slingbacks – shoes that may match what you want (assuming they have very thin, very high heels.) In the sections below, we’ll dive into the specifics of semantic search, including how it works, more examples of how it’s used, and why it’s important.

Semantic Search Examples

When it comes to deriving meaning from words, it’s all about semantics – especially when you’re searching for information online. As with most things, it’s easier to show versus tell. Here are some examples of semantic search in action:

• When you type “restaurants” into a search engine, semantic search uses your location to provide results for eateries near you, not just webpages containing the word “restaurants.” 
• If you search for lyrics like “Hello, it’s me,” a semantic search engine can match that to the intended song “Hello” by Adele, displaying the title and full lyrics. 
• When you search for “Creuset vs. Staub dutch ovens,” semantic search recognizes your intent is to compare those two brands of cookware, so it may produce product reviews and comparisons, along with product listing and matches. 
• And, as we noted above, if you’re searching for “black stilettos“, a semantic search engine understands that phrases like “spiked heels” and “pin heels” refer to the same type of slim, high-heeled shoes. 

Searching Before Semantic Search

Before semantic search, the reigning search technology was keyword search, which matched the words in a user’s query to the words present on a webpage or document. The pages containing the highest number of matching keywords would be returned as the top results.

This approach had major limitations. Since it couldn’t account for searcher intent or query context, it would get easily tripped up. Synonyms, paraphrases, or phrases expressing the same concept differently than the literal keywords on the pages were ranked lower in the results or missed entirely.

Ambiguous queries like “Where is the world cup?” could return completely irrelevant results just because those words were present, such as “Where in the world is my cup of coffee?” Adding to the madness were shady search engine optimization (SEO) techniques like keyword stuffing and poor-quality content optimized just for keywords, not users.

Google began working towards a semantic search engine as early as 1999, but threw everything behind semantic search with the introduction of the Knowledge Graph in 2012. Google’s Hummingbird update, introduced in 2013, replaced their previous ranking algorithms, a fundamental shift that went beyond keywords as the sole understanding of relevance. 

Hummingbird attempted to understand the relationships between named entities like people, places, organizations and products and abstract concepts (e.g. distance, quantity, world peace, etc.) These relationships, ultimately, are what informed the ranking algorithm.  The key distinction from regular keywords is that entities represent unique, identifiable objects or concepts, rather than just words or phrases.

Semantic search now powers product discovery on retail sites and provides intelligent response to complex queries. It’s what allows users to have humanlike conversations with machines too. 

By leveraging techniques like NLP and ML, semantic search accounts for relevance signals that pure keyword matching inevitably misses. Users can search in ways that are comfortable to them – with using synonyms, natural language, and context.

Semantic Search vs Vector Search vs Keyword Search

There are some distinctions between vector search and semantic search when compared to keyword search. They all play a role in helping users find information, but understanding how each of these three technologies work can help you select the right approach for your business or website.

Vector search 

Vector search encodes searchable (unstructured) data into numerical representations called vectors.  A vector representation maps a piece of text (word, phrase, sentence, document, etc.) to a point in a high-dimensional vector space. Vector points are a list of numbers that represent the semantic properties of the text in your query.

Semantic Search 

Semantic search refers to the broad capability of understanding both the user intent and contextual meaning of a query. You need vector search to enable semantic search, but there’s more to semantic search than just vectors. A full semantic search engine incorporates other technologies like NLP to understand query semantics and ML to identify entities and relationships in queries and data. It also factors in contextual signals like location, previous searches, and ranking techniques to determine most relevant semantic matches, plus it needs a good user interface.

Traditional Keyword Search

Traditional keyword search is much more literal than semantic or vector search. It tries to match the exact keyword or phrase to a source document. The keywords are the sole source of relevance – the top ranking factor – for the search engine. This is how why you may end up with zero results if there’s not an exact match. It’s why if you search for “black stilettos” you may get zero results even if a retailer carries hundreds of shoe styles sporting black stilettos. The search engine doesn’t know that those 6-inch high-heeled “onyx” sandals are a match.

How does Semantic Search Work?

As we touched on above, semantic search leverages vector embeddings to map words and phrases to numerical vectors based on their meaning and context. It applies ML techniques like vector similarity to match the vector representation of the user’s query against content vectors in the search index.

The closest vector matches are shown to the searcher as the top results. The search engine prioritizes semantic relevance over keyword matching alone, which is why sketchy SEO techniques like keyword stuffing don’t fool semantic search engines into recommending irrelevant or low-quality content. 

Advanced semantic engines combine this vector approach with NLP models to further enhance understanding of query intent and entity relationships. They often incorporate contextual signals like location, previous searches, and (in the case of ecommerce websites) past purchases. By processing the “semantics” rather than just the literal keywords, semantic search can comprehend and satisfy the user’s true information need.

Does Semantic Search Impact SEO?

The rise of semantic search doesn’t make traditional SEO practices obsolete, but it does require some adaptation for digital marketers. Since semantic search accounts for intent and meaning versus keywords alone, the myopic focus on cramming web pages with keywords isn’t very effective as an SEO strategy.

Instead, a topic-driven content approach is much more effective. It’s also better for human users since it requires you to create high-quality, comprehensive content. You should provide a depth and breadth of content that contains semantically relevant terms and concepts. For marketers that means: 

• Optimizing for topical relevance over specific keywords: Instead of trying to rank for “red panda facts”, create content that discusses the panda’s habitat, diet, behaviors, conservation status, etc. Use relevant terms naturally throughout.
• Incorporating related concepts and entity relationships: For content on “project management software”, discuss related concepts like agile methodologies, task tracking, team collaboration tools, etc. Establish the relationships between the core topic and these pertinent entities.
• Leveraging natural language signals for queries: Analyze how users might phrase queries around your topics using natural language. For “chocolate cake recipes”, they may ask “how to make moist chocolate cake” or “best easy chocolate cake recipe.” Incorporate these natural terms and sentences.

Ultimately, semantic search rewards content that provides a good user experience by satisfying the searcher’s true information needs. Stuffing keywords into content with brute force simply doesn’t cut it anymore. Semantic search forces an overdue prioritization of user value over gaming the system.

List of Semantic Search Engines

Semantic search is still an emerging technology, but several major search engines and platforms have incorporated semantic capabilities including:

• Google’s RankBrain, which uses ML to help decipher user intent and provide relevant results through semantic understanding.
• Microsoft’s Azure Cognitive Search, a cloud search service that employs AI models like the SBERT sentence transformer for semantic relevance.
• Amazon’s Kendra, which is an enterprise search engine that uses NLP to interpret queries and find semantically related content.
• Monetate Personalized Search, an AI-powered site search solution that uses advanced NLP to decipher search intent and provide hyper-relevant product discovery through semantic understanding.

Why is Semantic Search Important?

Semantic search creates more human-friendly, intuitive search experiences than keyword search. By accounting for the complexities and nuances of human language, it’s better at understanding user intent. This makes for a far more satisfying experience for searchers.  

The AI-powered models behind semantic search engines allow these tools to derive meaning from user queries. It’s a more natural experience, overall, since the search engine can comprehend concepts and context rather than just literal terms. 

As semantic capabilities continue advancing, user expectations around intelligent search interpretations will continue to increase. Companies providing semantic search can boost metrics like engagement, conversion, and brand satisfaction.