The way we look for information and products online is continually evolving. In the past but today as well, a lot of searches are based on specific keywords and key phrases. However, more and more often, semantic search is available to customers. For many users, this is a more intuitive and user-friendly way of finding the right products or information.
Semantic search is possible mainly due to the development of artificial intelligence and natural language processing (NLP) – related technology that allows those algorithms to “understand” and respond to queries that are written in a way that’s not based on specific keywords (i.e., more descriptive ones). In this article, we’ll have a closer look at semantic search and tell you a bit more about this technology. If you run an online store or any other online-based business, semantic search can help you improve UX (user experience) in your store and make it more effective at attracting new clients.
However, before we get to that, let’s have a look at what semantic search actually is.
What is semantic search (and how does it work)?
Semantic search is a relatively new approach to searches and queries conducted online by both companies and individual users. This search technique doesn’t rely on keywords but rather on the intent or the meaning behind a specific query. NLP algorithms try to analyze the query to find patterns and correlations that correspond with specific products or pieces of data or information.
Here’s an example:
- Traditional keyword-based query: Nike AirMax 90 Black
Here, we have several keywords: a brand name, a product name, and the product color. You don’t need AI to conduct such a search because this particular user perfectly knows what they are looking for. The situation changes, when the user doesn’t know what they are looking for, but they still have some need that needs to be satisfied.
So, as an alternative, they can use semantic search to find what they are looking for:
- Semantic search query: Best smartphones for photography purposes
Here, the situation is different, as there can be dozens of products matching this description. This is where AI steps in the game. The NLP algorithm analyzes the query and recognizes that the customer is looking for a smartphone that’s good for photography purposes, meaning it has a high-quality camera, possibly with different options for different settings (e.g., portrait, night photos, panorama, etc.).
Now, the algorithm can find products that match the description. And since the customer is looking for the best smartphones, the algorithm can also look at the reviews of each model. As a result, the customer gets a list of smartphones that match their description and are highly valued by other users. That’s exactly what they’ve been looking for. And that’s the essence of this search technique. You find the right product even if you don’t know what you are looking for.
Semantic search: Main elements
Even though the description seems simple enough, from the technical perspective, you need to consider several important technologies that make semantic search a seamless and user-friendly solution. Here are the crucial ones [1]:
- Natural Language Processing (NLP): NLP is used to analyze the query in order to get its essence (intent, meaning). NLP algorithms analyze all the words used in the query and look for patterns and relationships between them.
- Knowledge graphs: Their goal is to map out connections between different pieces of data so that adequate search results may be provided to the user.
- Machine Learning: ML is a frequent component of semantic search algorithms; this AI-related technology is used to improve the search results over time by learning from hundreds of user queries and the feedback provided by them (for instance, the semantic search algorithm can ask the user whether they are satisfied with the results they got).
- Knowledge base: The search algorithm still needs access to the database/knowledge base of your products or pieces of content so that it can connect the query with the product/article in your company.
How is natural language processing used in semantic search?
NLP is the main component of every semantic search algorithm, allowing it to “understand” and respond adequately to that kind of search. Here, several elements play an important role [2]:
- Tokenization: This technique is essentially based on breaking down a text into individual words or phrases (tokens). That’s the first step in understanding the query.
- Part-of-Speech Tagging: The algorithm assigns parts of speech (e.g., nouns, verbs, and adjectives) to each token, thus allowing the search engine to understand the grammatical structure of the query.
- Named Entity Recognition (NER): This technique identifies and categorizes the entities that were found in the query (e.g., people or locations)
- Word embeddings: These are representations of words that capture their meanings and relationships based on the available context. Word embeddings are important for the algorithms to understand the similarities between different words.
All four solutions are vital for the algorithm to accurately interpret each query and provide the customer/user with relevant search results. In many cases, such an approach not only provides users with more comprehensive results but also shortens the time needed to find the right product or the right piece of information.
Semantic search on text and knowledge bases
Semantic search can be used to work with both standard text-based data and diverse knowledge bases. There are a few things to consider here:
Textual data
When it comes to text-based data, this kind of search shines when it comes to locating the right information in large volumes of text, especially unstructured ones.
Consider this example: Suppose we have a customer looking for skis for beginners. A typical keyword-based search would just provide them with search results that comprise this phrase in the description. A semantic search may go beyond that and provide them with articles on how to pick the right skis for a beginner in addition to relevant search results. This way, the customer can achieve two goals at the same time:
- Find relevant products
- Find out more about their area of interest
Knowledge bases
The situation is a bit different with knowledge bases; here, we usually deal with structured data to provide more accurate and relevant answers. Let’s use a different example: If you have a medical knowledge base, and you have a user looking for cancer treatment, the semantic search algorithm can provide them with more information on such topics as potential side effects, efficiency rates, etc. For example, knowledge bases for Amazon Bedrock support semantic search [3].
Semantic search vs. keyword search
To summarize this part, it’s vital to compare these two different approaches and see where they differ:
| Traditional keyword search | Semantic search |
| Here, the search algorithms rely on exact word and phrase matches. If the key phrase doesn’t contain a specific element, you will likely not find it among the search results. With specific searches, that’s not a problem, but if you’re looking for something more general or something that can have many different meanings, things can get more complicated. | This type of search focuses on the intent and the meaning of a specific query, which is possible thanks to AI-related technology known as NLP. Frequently, semantic search algorithms also take the previous activity of a given user into account so that they can provide them with more accurate and relevant results. |
Users that have experience with the latter type of search value this technology for:
- Improved relevance of search results
- Broader scope of received search results (going beyond standard keywords)
- Improved user experience (because the user can get the information that’s relevant to their search even if they didn’t know about that in the first place)
What are the benefits of semantic search?
We’ve already mentioned some of the main benefits of this type of search, but let’s summarize the most important ones in one place:
- Contextual understanding: That kind of search is all about the context and meaning behind each query. This means is can provide users with more accurate and relevant results vs. standard keyword-based search. This solution makes a case for itself especially in complex domains such as the medicine, law, and science.
- Personalization: More advanced semantic search algorithms can take into account not just the query itself but also the activity of a given user and get insights from their history, preferences, and behavior. This way, the search results each person gets can differ and be more tailored to a given user.
- Improved efficiency: When conducted correctly, semantic search can reduce the need for multiple searches, thus allowing users to get what they were looking for on a first try. This saves time and makes the searching process far more effective.
- More natural type of search: Lastly, with this kind of search users don’t need to think about keywords – they can naturally describe what they are looking for in their own words; the algorithm does the rest.
Wrapping up
Will semantic search replace keyword-based search altogether? Probably not; both types of searches have their pros and cons. Don’t think of this type of search as a replacement for keyword search; rather, it is a worthy alternative for specific scenarios. There is no doubt that semantic search can be extremely helpful when it comes to going through extensive knowledge bases or looking for hidden correlations between diverse pieces of data.
As the volume of digital information continues to grow, the importance of semantic search will only continue to grow. This, together with other AI-related technologies and solutions, can lead to more advancements in the way we look for information or products online. If you run an online-based business, we encourage you to explore the possibilities that semantic search offers even today. It can be a good way to improve UX and the effectiveness of your business.
[1] TechTarget.com, semantic search, https://www.techtarget.com/searchenterpriseai/definition/semantic-search, accessed July 22, 2024
[2] TechTarget.com, semantic search, https://www.techtarget.com/searchenterpriseai/definition/semantic-search, accessed July 22, 2024
[3] AWS Machine Learning Blog, Knowledge Bases for Amazon Bedrock now supports hybrid search, https://aws.amazon.com/blogs/machine-learning/knowledge-bases-for-amazon-bedrock-now-supports-hybrid-search/, accessed July 22, 2024
