Recently, there has been some discourse on X and other platforms about Google not being as helpful as it used to be. While some blame this on Google's algorithm updates and AI integration, the reason could also be semantic search, which, despite understanding intent, may often struggle to show desired results.
In domains like healthcare and finance, this is even more concerning as the niche terminology is often misunderstood by semantic search algorithms. As a result, businesses may have trouble effectively optimizing their content for the new search models.
However, there's not much of a choice, as semantic search is the new normal. Even in AI Overviews, only 6% of results actually include the search query. The rest are intent-based rather than keyword-centric. So, it's imperative for you to master semantic search going forward.
In this guide, we cover the intricacies of semantic search and share tools that you can use to overcome the challenges this search model brings. Plus, we describe the benefits of semantic search not just for users but also for you as a business.
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Semantic Search 101: Why It's Changing the Game
According to Google, semantic search "focuses on understanding the contextual meaning and intent behind a user's search query, rather than only matching keywords." In simple words, when you search for something, the search engine not only matches the keywords you entered with results (as it used to) but also makes sense of what you're searching for to show you the most relevant results.
The search engine's understanding isn't just limited to your intent. It also looks at the relationship between words. For example, when you search for "cat food for kittens," the search engine understands that "cat food" is a broad term, and "kittens" narrows it down.
So, you will see results with products for younger cats. In instances where you have made any previous searches, the search engine would also consider them in its outcomes.
For example, if you searched for "cat food for kittens" previously and then searched for "best food for cats," you would see results for kitten-specific products. In addition, semantic search also accounts for your location. Let's say you're searching for Australia. Google will show you stores in your country where you can buy cat food for kittens.
While this is a relatively simple example, semantic search goes even deeper. Let's say you're a filmmaker looking for a video editing software. You search for "best video editing software," and the results show you different options.
However, if your query is "best film editing software," the results will be more specific to your needs. The search engine has understood the context of your search.
By context, we mean the search engines know what a filmmaker needs from editing software, such as advanced features for special effects and color correction. So, it will show you results for tools that match these specifications instead of general video editing software.
The Core Components of Semantic Search
A number of AI technologies are used to power semantic search, with Natural Language Processing (NLP) being the most well-known. In the simplest words, NLP is the technology that allows machines to understand and interpret human language. Think of it as a translator or an intermediary. It takes your query, analyzes its components, and then translates it into a language that the search engine can understand.
It is supplemented by embeddings, nearest-neighbor algorithms, and cosine similarity. These technologies map the relationships between words and phrases so that search engines can grasp the context behind your queries.
Semantic search components
Let's look at each of these components in detail.
Natural Language Processing (NLP)
Just like you may speak English or your regional language, computers also have their own language: machine language. NLP acts as a bridge between these two mediums and makes communication possible.
It does so in two phases: data preprocessing and algorithm development.
- Data Preprocessing: In this phase, data is prepared to be understood by machines. For example, stop word removal is a part of this process, which refers to removing common words so that the algorithm can focus on the more meaningful words in a sentence. Similarly, words are "lemmatized" or "stemmed" to reduce them to their root form (such as "sleeping" to "sleep"), making it easier for the algorithm to understand.
- Algorithm Development: This phase involves developing an algorithm to process the data. Rule-based systems were used in early NLP, where a set of rules were defined to interpret a query. A newer approach is a machine learning-based system in which algorithms train on a certain dataset and then improve their own rules with repeated processing.
Embeddings
Embeddings are an NLP technique in which text is converted into numbers or vectors. In a LinkedIn video, Susannah Kate Plaisted, Senior Product Manager at Salesforce, explains that vectors are basically coordinates that represent words in a multi-dimensional space.
Similarly, Bidirectional Encoder Representations from Transformers (BERT) creates embeddings with a context. In this technique, each word is represented by a vector that accounts for the words before and after it in the sentence. So, it can determine the different meanings of the same word in different contexts.
Cosine Similarity & Nearest-Neighbor Algorithms
Cosine similarity is a mathematical measure of how similar two vectors are. In NLP, it helps compare the similarity between documents or sentences based on their word embeddings. It determines if two vectors are pointing in the same direction by measuring the cosine of the angle between them.
Nearest-neighbor algorithms use cosine similarity to find the closest vector or document to a given input. A practical example of these algorithms in action comes from sentiment analysis, in which we identify the sentiment of a new review by comparing it to other reviews with known sentiments.
How the Core Components of Semantic Search Work In Sync
Together, embeddings, cosine similarity, and nearest-neighbor algorithms form the vector search. Basically, we can search for vectors (search engine results) that are closest to a given vector (our query). This way, we can find similar documents or sentences based on their underlying meanings rather than just keywords or phrases.
Semantic search takes this a step further by bringing intent modeling and context relationships into the picture. A good example of context relationships is knowledge graphs.
A knowledge graph is a graphical representation of the relationships between different real-world entities. It comprises notes, edges, and labels. The nodes may be places (such as The Met), people (like Michael Jackson), or events (like the Olympics). Edges define the links between these nodes, such as The Met is located in New York City. Labels provide additional information on the nodes and edges, like opening hours for a museum.
Google collects all information about an entity from public sources and then shows it in the knowledge panel, which is the box on the right side of a search engine results page:
Components of Semantic Search
The idea behind this approach is to provide users with as much intent-centric information as possible.
A Real-World Example of Semantic Search
Google isn't the only one using semantic search. An excellent case study of semantic search utilization comes from LinkedIn. Previously, the platform's search feature struggled with complex queries and was unable to show relevant results.
Then, the AI team introduced semantic search, which works in two layers.
Layer 1: Retrieval Layer
In the first layer, the system chooses only a few candidate posts (rather than searching through millions of posts) based on two criteria.
- Token-Based: The system uses an inverted index to narrow down posts that have the same keywords as the query. There's no semantic understanding in this step.
- Embedding-Based: Now, the semantic search comes into play. LinkedIn uses a two-tower model for semantic comprehension: query tower and post tower. The query tower embeds user data and search terms, while the post tower embeds post data. LinkedIn's model then searches for a match between the two towers using the Approximate Nearest Neighbor (ANN) algorithm.
Layer 2: Multi-Stage Ranking Layer
After the first layer has narrowed down the output options to fewer posts, the ranking layer then refines the results using these two stages:
- L1 to reduce thousands of posts to a few hundred
- L2 to score these posts based on various factors like relevance, quality, engagement, and more
LinkedIn's semantic search engine has increased engagement by 10%, which proves just how effective the search model is.
Lexical, Semantic, and Vector Search: Understanding the Differences
At times, it gets a bit confusing to differentiate between different types of search methods. Although they all fall under the umbrella of information retrieval and search engines, they have key distinctions that set them apart. Let's look at them individually.
Lexical Search: The Old Guard
Lexical comes from ''lexis'', meaning words. As the name gives way, lexical search is all about matching words or keywords as they're known in the SEO space.
If a user searched for ''best plants to grow in my home garden'' and you had the exact same phrase in your content, congratulations! Your page will likely rank higher than others. That's how lexical search works.
However, it doesn't account for intent or context. Your article may simply use that phrase in passing rather than being a comprehensive guide to growing such plants. So the user wouldn't find helpful information.
Lexical search also has difficulty handling typos, variations in spellings, singular/plural forms, and synonyms. It won't provide relevant results if any of these elements differ from the query.
Semantic Search vs. Vector Search
The (more advanced) alternative to lexical search is semantic search, which we see in LLM SEO practices. While lexical has to do with words, both vector and semantic pertain to the meaning of words.
Both these search types are similar in that they seek meaning beyond the words. For this, they employ embeddings, which we have described earlier.
However, both these techniques differ in their approach. Vector search uses mathematical elements like cosine similarity to calculate the distance between words in a query and a document. The closer they are in space, the more relevant the results. It's the search that's in action when you search for specific emails in your inbox.
In contrast, semantic search uses intent and relationships as the basis for understanding queries. As we've explained earlier, it uses context, location, previous searches, and intent to show results.
Hybrid Search
Just because it's old school doesn't mean lexical search is obsolete. Nor is semantic search perfect. That's why many developers use a combination of lexical and semantic search, commonly known as hybrid search.
In the LinkedIn case study above, you can see how hybrid search is used for post-retrieval. The first step is token-based retrieval, which is purely lexical. Then, embedding-based retrieval kicks in, which is the vector search that uses the mathematical principle of Approximate Nearest Neighbor (ANN) to fetch the best results.
Why Businesses and Users Need Semantic Search
While semantic search is important in search engines due to the rise of AI SEO, it's also vital in business spaces, especially where data is unstructured and complex.
Academic archiving is a good example. Institutions like universities or libraries have extensive databases that contain information in different languages from diverse sources. This information also has to be accessed by thousands of people from across the globe.
Semantic search helps users find their desired information from these databases. For these institutions, it means better organization and improved user experience.
The use of semantic search by LinkedIn also has similar benefits. While the platform has increased its engagement rate by 10%, users enjoy the improved search experience. They can now find desired content faster, which can help them learn, foster industry connections, find jobs, and network.
As we've previously mentioned, semantic search also accounts for location. It's not just the geographical aspects of location that matter; rather, it's the context of location. This could include cultural nuances, political factors, multilingualism, and so on.
For example, when you search for ''mother of bride dresses India,'' Google shows ethnic Indian dresses.
Google search results
However, the same search for the US location shows Western-style gowns. Google's algorithm uses cross-lingual embeddings to understand user intent. Since it considers intent, it knows the cultural context behind the search query for each region.
Google search result
Again, these results benefit both the users and businesses. The former find what they're looking for, while the latter enjoy website visits and potential revenue.
Breaking the Biggest Challenges in Semantic Search
When dealing with semantic search, such as Perplexity AI SEO or a proprietary homegrown system, two challenges often arise. Here's how to tackle them.
Problem: False Positives and Irrelevant Results
Semantic search isn't always perfect; it may show results that are irrelevant to the user's query. These false positives can frustrate users and cause poor retention.
A hybrid search model is the ultimate solution for dealing with false positives. As we've explained, a hybrid of lexical and semantic search minimizes the chances of irrelevant results.
You can further enhance your model with advanced ranking layers, such as Elastic's Rerank model, which uses the DeBERTa v3 architecture to refine results. In the developers' testing, the model boasts a 40% uplift in retrieval tasks, which means 40% fewer false positives.
"Performant and Efficient: The Elastic Rerank model outperforms other significantly larger reranking models. [...] Our detailed testing shows a 40% uplift on a broad range of retrieval tasks and up to 90% on question answering data sets." https://t.co/aUM6EtWnfH
— Costin Leau (@costinl) December 12, 2024
Problem: Scalability
As datasets grow, it becomes harder to scale semantic search systems. The larger the dataset, the more computing power is required for processing and retrieving results.
You can address this challenge using tools like Qdrant, an open-source vector similarity search tech that is optimized for high performance and scalability. In addition, ANN algorithms, such as the one used by LinkedIn to scour relevant results through billions of posts, can also help improve scalability and speed up search processes.
How to Succeed with Semantic Search Optimization
Semantic search optimization needs to be a part of your AI SEO strategy now that AI Overviews and LLMs are the users' choice of search platforms. Use these tips to ace it.
Focus on User Intent, Not Just Keywords
This one's quite obvious, but it's easier said than done. Most businesses are so used to the idea of keyword optimization that the shift to intent-driven content creation is a tad bit complicated.
However, you can simplify this process with Semrush. To start, use the Semrush Keyword Magic Tool to find terms your audience is using in the search bar. Being an AI keyword search tool, it provides a lot more information than just a list of words or phrases.
The most important of these details is the user intent, which the tool shows alongside each keyword. Keep this intent in mind when writing content for semantic search.
Keyword Magic Tool
You can then export these keywords into the Keyword Strategy Builder, which will create topic clusters and relevant subtopics that you can incorporate into your content. Remember how we said that semantic search looks at the relationship between words and topics?
Keyword Strategy Builder
This is exactly what the tool helps you tap into. As you include more closely related topics in your content, the content's internal relation score will increase, making it easier to rank higher in semantic search results.
Enhance Content with Related Concepts
Don't just settle for related topics. For example, including sections on history, recent developments, use cases, and case studies in an article about a technology like blockchain is just one step of semantic search optimization.
You should also include related terms, synonyms, and concepts for added comprehensiveness. For example, besides blockchain, mention terms like cryptocurrency, decentralized ledger, smart contracts, and distributed databases. NLP algorithms have an easier time understanding the context and relevancy of such semantically rich content.
You can use the Semrush On-Page SEO Checker for recommendations. It provides an exhaustive list of SEO suggestions, including those for strategy, user experience, backlinks, and technical SEO.
On-page SEO checker
There's also a semantic SEO section where you can find ideas for enriching your content. Use these recommendations to optimize your web pages further for semantic search.
Semantic ideas
Create Answer-Driven Content
For your content to meet user intent, it needs to answer a certain question, whether it's informative or transactional. Include question-based sections in your content to meet this intent.
For example, you can create an FAQ section in every article or categorize the content into steps. You may also give direct answers to questions in your content.
Tools like the Semrush Keyword Overview can help you find questions related to a seed keyword. It also shows you the intent for these questions so you can answer accordingly.
Keyword Overview
Semrush also has a free SERP Checker Tool that you can use to see the search results for any keyword. Focus on the ''People also ask'' section to find semantically relevant questions to add to your content. When answering these questions, relate the content to the overarching topic to enhance its context for semantic search algorithms.
Building Semantic Search Systems
If you want to create a semantic search system like LinkedIn's multi-layered model, you'll need vector databases to store data that can be used to represent and analyze the meaning of words in documents.
Plus, you'll need an orchestration framework for your system. Langchain is one such framework that can help you build reasoning and context-aware semantic search systems. Similarly, Elastic Stack is an open-source search engine and vector database where you cannot only store data but also tune it to deliver outputs with the utmost relevance.
Making Semantic Search Work for You
At Influencer Marketing Hub, we have seen a 525% increase in revenue from AI-powered search engines. Why? They help us provide intent-based answers to your audience's queries.
Going forward, this would have to be the norm for every business as semantic search is becoming the new standard for search engines. Using trending keywords in your content won't be enough; you'll also have to semantically enhance the content containing them. With the tips and tools we've discussed above, doing this will be simpler.
Frequently Asked Questions
What is semantic search, and how does it work?
Semantic search is all about understanding the meaning and context behind your query, not just matching keywords. It uses AI, natural language processing, and sometimes embeddings to figure out intent and context.
What are the main benefits of semantic search over traditional keyword search?
Semantic search gives you way better results because it understands context and intent, not just exact words. It's especially useful for complex queries, where it connects the dots between words and topics. Plus, it handles synonyms and variations, so you don't need to guess the exact phrasing.
How does semantic search use embeddings to improve results?
Embeddings turn words, phrases, or even entire documents into numbers that reflect their meaning, not just their literal text. Semantic search uses these embeddings to compare how similar things are in meaning. So instead of just matching "car" with "car," it knows "automobile" and "vehicle" are basically the same thing.
What tools are best for implementing semantic search?
Some of the top tools for semantic search are ElasticSearch (vector search features), MongoDB (with its full-text search capabilities), and specialized vector databases like Pinecone or Weaviate. The choice depends on your needs. Elastic is great for hybrid search, while Pinecone is a good option for high-performance vector searches.
How does a hybrid search combine semantic and keyword methods?
Hybrid search uses semantic search to understand intent and context and applies keyword matching to narrow down the most relevant results. So, the output is more accurate and relevant than if any of the two models were used individually.
What industries benefit most from semantic search?
Industries like healthcare, legal, eCommerce, and education really benefit from semantic search. For example, in healthcare, it helps find relevant medical papers or case studies quickly. In e-commerce, it improves product recommendations. For legal and educational fields, it facilitates the retrieval of information amidst tons of data.
Can semantic search handle ambiguous queries effectively?
Semantic search is great at disambiguating queries. It looks at the overall context of your search, not just individual words. So, if you search for something like "jaguar," it can understand whether you're asking about the car, the animal, or even a software tool based on your other keywords or past behavior.
What is the role of NLP in semantic search?
Natural Language Processing (NLP) helps the search system understand human language. It allows the search engine to process and interpret words in context and grasp nuances like intent.
What are the limitations of semantic search in enterprise applications?
Semantic search in enterprise apps can face some challenges, like high implementation costs and complexity. It might struggle with niche or highly technical data, too. Plus, tuning embeddings for specific industries can be tricky. You'll need continuous fine-tuning to keep your search results accurate.
What is the difference between semantic search and vector search?
Vector search uses mathematical vectors to understand the relationships between data points, while semantic search focuses on meaning and context within a dataset. So, the former is more quantitative, while the latter is more qualitative. Also, vector search can handle a wider range of data types and sources, whereas semantic search works best with structured data.
