Semantic search: How it works and who it's for

As search engines become increasingly sophisticated, they can do more than just recognize words - they can understand the actual intent and context of a query. Semantic search is one of the prominent technologies fueling this transformation for users as well as businesses. Let’s take a look at what semantic search is and who benefits from it.

What is semantic search? 

Semantic search is a search engine technique that interprets the intent behind a user's query by understanding the context and meaning of words. It goes beyond traditional keyword matching, considering the relationship between words to deliver more accurate and relevant search results. This approach enhances search engines' ability to comprehend user queries and provide more contextually appropriate information.

In a nutshell, it’s a way of decoding the intent behind a search query rather than just the literal keywords they use. Semantic search focuses on the meaning, context, and relationship between concepts to understand what the user is seeking. 

Let's take an example. If a user searches for "how to get rid of pimple fast," a traditional keyword-based search engine would return results that include websites with the keywords "pimple", "fast", and "how to". However, a semantic search engine would analyze the meaning of the user's query and return results that are relevant to the user's intent. This could include blogs or videos that explain how to remove pimples quickly or product pages for acne treatments and products. 

The nuts and bolts of semantic search engines 

Below are the three main underlying principles that drive semantic search.

1. Vector Similarity 

Vector similarity is a measure of how “close” two things are in meaning. Semantic search uses vector similarity to match results with queries by finding results that have similar vectors to the query.

The documents are represented as vectors, and the similarity between the query and the document is calculated using a distance metric.

The vectors are then compared to one another to find relationships and patterns. This approach can be used to find documents that are similar to one another or to identify the key themes in a collection of documents.

This technique is incredibly powerful, as it allows for far more nuanced and sophisticated search results than keyword-based search engines can provide.

Vector space modeling can be used to overcome some of the limitations of keyword-based search engines. For example, it can deal with synonyms and polysemy (multiple meanings of a word) more effectively. It can also handle misspellings and different word forms (e.g., plurals) better than keyword-based search engines.

2. Context

Semantic search engines work by decoding the context of a search. Context includes where the user is located, what their past searches have been about, and what their preferences are.  It will also draw relationships between real-world ideas and concepts.

Let's take an example. For a query like "the best shoe", a semantic search engine will draw the connection between this query and the user's location and past search behavior. In case they searched for "flatfoot treatment" in the past, the search engine will take note of it and tailor the search results accordingly.

Semantic search understands context on both individual and collective terms. This means that not only will it surface shoe recommendations that aid people with foot problems, it will also take cues from other users' data. It re-ranks results on the basis of how other users interact with different search results. 

3. User intent

Semantic search understands intent and ranks search results in the order of relevance. It excels at disambiguating queries. In simpler terms, if a user searches for "apple," the semantic search engine can determine whether the user is looking for information about the fruit or the company.

Semantic search engines rely on a number of different signals, including: 

• The structure of the query: The order of the words in a query can provide clues about the user’s intent. For example, a query like “cat food” is likely to be about buying cat food, while “food for my cat” is more likely to be about finding recipes.

• The type of information being sought: Semantic search engines can use the type of information being sought (e.g., videos, images, documents) to improve the relevance of results.

• The user’s location: The user’s location can be used to provide results that are relevant to that particular area (e.g., local businesses or news stories). 

• The user’s history: The search engine can use information from the user’s past queries and clicks to better understand their current intent

What makes semantic search different from keyword-based search? 

The difference between semantic search and keyword-based search lies in the way each approach interprets a user’s query. Semantic search looks beyond the literal meaning of words to derive the intent behind a user’s request, while keyword-based search simply matches the query with documents that contain those same keywords. Where semantic search looks at the meaning behind the query, the keyword-based search looks at individual words in isolation to match results.

This distinction is important because it reflects the fundamental difference between the two approaches: semantic search is designed to understand user intent and provide results that are relevant to that intent. On the flip side, keyword-based search is designed to match documents with queries, regardless of whether or not those documents are actually relevant to what the user is looking for.

Who is semantic search for? 

In short, any business that wants to stay ahead of the curve and offer their customers the best possible experience should consider using semantic search. However, there are some businesses that will benefit from semantic search more than others. For example, businesses with large inventories or those that sell products that are often purchased together will likely see the biggest benefits from using this technology. Additionally, businesses that are already utilizing NLP in other areas of their operations may find it easier to implement semantic search than those who are starting from scratch.

Conclusion - Semantic search is a powerful tool for enhanced search performance

There are many potential benefits of implementing semantic search for ecommerce businesses, including improved search accuracy, increased customer satisfaction, and decreased customer support costs. Semantic search can help improve the accuracy of search results by understanding the search intent and the context of the query. This can result in more relevant and targeted results, which can lead to increased customer satisfaction. In addition, semantic search can help reduce the need for customers to contact customer support, as they can more easily find the information they need themselves. 

Implementing a powerful search functionality is essential for ecommerce businesses in today’s hypercompetitive economy. But building a site search engine from scratch and maintaining it requires extensive engineering and financial resources. This is where AI-driven search and discovery solutions like Zevi can take your search performance to the next level. 

Zevi is an intelligent search engine powered by sophisticated AI, Machine Learning, and Natural Language search technologies. Zevi understands user intent and context to deliver personalized, accurate, and relevant results, thus enhancing customer experience. 

To get started, book your free demo today.