This article is all about the concept of semantic search, the practice of factoring in the intent and meaning behind a query when surfacing search results. Learn more about how you can use Shopify’s brand-new Semantic Search feature in the Search & Discovery app to apply this to your product catalog—and check out the latest Edition for even more product news.
Searching for information on the web isn’t an exact science. When you can’t think of the precise phrase or keywords to find your desired information, search engines can suggest web pages or online documents that don’t quite match what you’re looking for.
Sometimes, a practice known as semantic search can resolve this problem. This is a sophisticated form of processing search queries aimed at deciphering a user’s true search intent.
What is semantic search?
Semantic search aims to improve the relevance of search results by surmising the intent behind a user’s query rather than simply taking a query at face value. Semantic search contrasts with traditional keyword-based search by factoring in the meanings of words, relationships between concepts, image data, and other additional context surrounding a query.
What are the benefits of semantic search?
- Improved relevance and accuracy
- Understanding of synonyms and related terms
- Contextual comprehension
- Support for voice search and conversational queries
- Better user experience
Semantic search lets search engines optimize the power of their search algorithms and deliver more relevant results to users. The key advantages of semantic search include:
Improved relevance and accuracy
Semantic search seeks to understand a user’s intent when typing a search query into a search box. It does this in part by focusing on the semantic meaning of words rather than the most common literal meaning. For instance, the word “foundation” could refer to the base of a building, a type of makeup, or an advocacy organization.
Semantic search takes all of a word’s meanings into account if a user uses it in a search query. This helps produce search results that match the user’s actual needs rather than just surfacing pages or products containing specific keywords.
Understanding of synonyms and related terms
Semantic search considers synonyms, related topics and terms, and variations of words when processing a search query. This helps the search engine better understand what users are actually searching for, even if their queries are vague or imprecise.
Semantic search uses natural language processing (NLP) to assess a series of queries as a single ongoing conversation. This helps it determineuser intent when a person types a follow-up to the search engine’s initial response.
Support for voice search and conversational queries
Thanks to its ability to understand natural language and context, semantic search can power voice searches and conversational chatbot queries, making it easier for users to find information in multiple ways.
Better user experience
Thanks to its focus on a searcher’s intent and its comprehension of semantics, wordrelationships, and sentence structure, a semantic search engine may increase the likelihood of producing relevant results. This can lead to greater user satisfaction.
How semantic search works
- Natural language processing (NLP)
- Intent understanding
- Contextual analysis
- Knowledge graphs
- Ranking and relevance scoring
- Machine learning
Semantic search uses multiple tools to decipher semantic meaning and answer user queries. Here’s a breakdown of how semantic search operates:
1. Natural language processing (NLP)
Semantic search starts by tapping into large language models, which help computer systems comprehend the words and phrases that make up human speech. NLP lets a semantic search engine analyze a search query and break it down into its constituent parts, including its syntactical structure, or the way the words combine to form a sentence.
2. Intent understanding
Next, a semantic search system attempts to identify the user’s intent or purpose behind the search query. It considers what the user is trying to achieve or what knowledge they are seeking.
3. Contextual analysis
Understanding the context of a user’s words and phrases helps a semantic search engine generate useful results. Semantic search seeks context by considering the surrounding words and phrases—along with the syntactical structure—to determine the intended meaning of a term.
4. Knowledge graphs
A knowledge graph is a structured (typically graphical) representation of information used to map relationships between words, entities, concepts, and images. Semantic search engines use these graphs to understand the context and relationships between different pieces of information.
5. Ranking and relevance scoring
A semantic search engine makes sense of a query and the available context, then ranks search results based on their perceived relevance to the user’s intent. This relevance scoring considers various factors, including semantic similarity and contextual relevance.
6. Machine learning
Semantic search engines continually refine their processes through machine learning. They try to analyze users’ satisfaction by monitoring follow-up queries. For example, Google, the world’s leading search engine, sometimes contacts users and asks them to rank their satisfaction with searching on its platform.
How search platforms use semantic search
- Widening focus through the use of synonyms
- Understanding entity relationships
- Contextual information for local search
- Identifying conceptually related results
- Semantic markups and rich snippets
Here are some ways that search engines and other search-based platforms, such as storefronts, use semantic capabilities to present results that matter to a user:
Widening focus through the use of synonyms
When a user enters a search query, a semantic search engine identifies synonyms and related terms to broaden the search. For example, if the user searches for “smartphone,” the search engine might also include results related to “mobile phone” or “cellphone.”
Understanding entity relationships
If a user searches for “apple,” a search engine employing semantic search understands the different meanings of “apple.” The user could mean the fruit, the tech company, or a colloquial phrase like “the apple of my eye.” The search engine will focus its results based on the likely context. This form of prediction helps semantic search work efficiently.
Contextual information for local search
If a user types a query such as “Chinese restaurants near me,” the search engine uses semantic analysis to understand that the user is looking for restaurants of a specific cuisine near their location, improving the relevance of the local search results. It can then point users to a business near their location rather than one that is far away.
As another example, if a user asks “what time does the Tigers game start,” the search engine may turn up different results depending on the user’s location and the time of their query. If someone types that phrase in Michigan on a summer weeknight, they probably want to know about the Detroit Tigers baseball game. If someone types that phrase in St. Louis on a Saturday morning in the fall, they might mean the Missouri Tigers college football game.
Identifying conceptually related results
Conceptual search engines piece together topics in ways their users might not. For a query like “how do satellites work,” the search engine identifies the concept of “satellites” and provides results that explain the process, related terms, and scientific understanding of the topic.
Semantic markups and rich snippets
Google and other search engines increasingly provide snippets and infographics to answer your search queries in addition to suggesting web pages that match your query. Search engines use schema markup to extract and display structured data in search results. This allows for richer snippets, which include additional information such as ratings, reviews, and event details, improving the user experience.
How to incorporate semantic search into your SEO strategy
- Conduct keyword research with semantic variations
- Create high-quality, comprehensive content
- Understand your target audience and their search intent
- Optimize for conversational queries
- Optimize for voice search
- Use structured data and schema markup
- Use topic clusters and pillar content
Semantic search’s growing role within leading search engines like Google and Bing has downstream effects on search engine marketing. You can prepare for the future of search by adjusting your SEO efforts with semantic search in mind. Here are some strategies for ranking on a semantic search results page:
1. Conduct keyword research with semantic variations
Conduct thorough keyword research that focuses on primary keywords and related terms, synonyms, and variations. Incorporate these semantic variations naturally within your content.
2. Create high-quality, comprehensive content
Develop content using SEO copywriting best practices. For example, cover a topic in-depth, addressing various aspects and related concepts, and structure the article in a way that’s easy for readers to digest.
3. Understand your target audience and their search intent
Conduct market research and create buyer personas to gain a deep understanding of your core audience. Create content that targets the behaviors, preferences, and specific intents behind their searches.
4. Optimize for conversational queries
Optimize your content to align with how people speak and ask questions. Consider addressing frequently asked questions and providing clear, concise answers in a conversational tone. FAQ sections can also help you optimize internal site search (within your web domain) in addition to gaining more web search traffic.
5. Optimize for voice search
Voice search queries are often more conversational than typed searches and may include more detail. Consider how people might phrase questions verbally and optimize your content to match these queries.
6. Use structured data and schema markup
As you build out your website and search engines index it, implement on-page SEO best practices like schema markup. Schema markup allows you to provide structured data that helps search engines understand the context and relationships between the different types of content on your page. This enhances the chances of your content being featured in rich snippets that rank high in Google searches.
7. Use topic clusters and pillar content
Structure your content strategy around topic clusters and pillar content. Create comprehensive pillar pages that document core topics and cluster related content around them, such as in-depth articles or short explainer pieces. This approach helps search engines understand the depth of your content on a specific subject and then match you to specific types of queries.
Semantic search FAQ
Why does semantic search matter for user experience?
Semantic search was created with the user experience in mind. Its designers seek to tap into the human mindset and make inferences about what information people are really seeking, rather than assuming the most literal interpretation of their search queries.
How do I apply the principles of semantic search to my SEO content?
You can apply the principles of semantic search to your SEO content by conducting keyword research with semantic variations, considering search intent when you create content, and optimizing for voice search and conversational queries.
Why is semantic search important for my business?
Semantic search is important because leading search engines are placing greater emphasis on it. Notably, Google has announced plans to make search results “more personal” by placing AI-powered semantic search responses atop many search engine results pages.