Have you ever thought about how search engines always know what you seek? That’s where semantic search comes in.

Semantic search is a data searching technique that uses artificial intelligence to understand the intent and contextual meaning of words and phrases in a search query to provide relevant content. This approach has become imperative in data-oriented technologies that require a fast and accurate solution. Semantic search is gradually changing the way we access information on the internet by reducing the gap between the language that humans use and that which machine understands.

Let’s continue our exploration of what semantic search is and how it works.

What is a Semantic Search Engine?

A semantic search engine is an elaborated information search tool that implements AI, such as NLP and ML, to interpret the query of the user. In contrast to conventional search engines utilizing keyword matching. Semantic search engines understand the meaning of the query by taking into consideration the intent and the contextual background. These AI semantic search engines operate by processing language, adapting themselves from users’ behavior and refining their knowledge on search terms to deliver better results.

Semantic Search vs. Keyword Search

Semantic search and keyword search refer to two different methods of information searching. While keyword search is based on exact word match semantic search understands the intention behind the search.

Advantages of semantic search over traditional methods:

• Enhanced efficiency of results in terms of relevance

• Improved Interpretation of user intent

• Information retrieval considering natural language questions

• Flexible definition of various and often vague concepts

For example, in a search for “apple,” semantic search can understand that the user is referring to the fruit or the technology company based on the results’ context and prior user searches.

Keyword-based search has its drawbacks. For synonyms, context, and intent, it gives poor results and sometimes searches for completely unrelated results. For example, typing “best running shoes for flat feet” will not retrieve close results that include the words “best running shoes for flat feet”, but will discuss it.

Vector Search vs Semantic Search

Semantic search and vector search are two related but distinct approaches to search. Vector search is one of the techniques that can be employed for executing semantic search but it can be applied for other goals as well.

Vector search is based on the use of numerical vectors in a multidimensional space to represent a piece of information. In this space, more similar items are positioned closer than those that are less similar. When a query is made, it is changed into vectors, and the system looks for the most relevant vectors to the query.

By contrast, semantic search refers to extracting the actual meaning and purpose behind the entered query. Although it primarily works with vector search as the technique, semantic search also employs natural language processing, and machine learning methods to understand the query and the content.

In other words, vector search is a tool that makes semantic search work, but semantic search is a broader concept that includes technologies and approaches to interpret and answer the query.

How Does Semantic Search Work?

Semantic search technology combines several advanced techniques to understand and respond to user queries:

• Natural Language Processing (NLP): 

  Analyse the structure and meaning of text
  Identifies entities, relationships, and context.
  
  

• Knowledge Graphs:
  Represent relationships between concepts.
  Enable understanding of complex queries.
  
  

• Machine Learning:
  Learns from user behaviour and feedback.
  Improves accuracy over time.
  
  

• Entity Recognition:
  Identifies and categorises named entities in text.
  Helps understand the subject of the query.

The process typically involves:

The process typically involves:

1. Query Analysis: The search engine recognizes the user’s request and takes into account such parameters as the language, geographic location, and searching history.

2. Intent Recognition: It identifies the nature of the query (informational, transactional etc.).

3. Contextual Understanding: The system incorporates the context of the query and the state of the user.

4. Semantic Matching: Rather than seeking precise matches of keywords, the engine seeks content that aligns with the meaning of the query asked.

5. Result Ranking: The entries are ranked according to their relevance to the interpreted query and not the number of times the keywords are repeated in the entry.

The use of these technologies makes semantic search answers more accurate, more relevant, and more personal than the simple Keywords based search.

Semantic Search Examples

Here are some of the examples of semantic search to help you understand better:

1. Google Search: When you t ype, “What’s the weather like?” Google knows you want to know the weather of your current location, not a phrase to search.

2. Amazon: A search for say ‘summer dress’ will pull up sundresses, beach coverups, and light summer dresses even if the search terms summer dress are not the specific words used in the title.

3. Netflix: This helps the platform to learn your preferences in terms of the shows you watch and recommend similar programs even if their names or descriptions do not match those of the liked programs.

4. Spotify: When a user types in ‘sad songs,’ it then displays depressing songs based on their lyrics and background, not necessarily songs containing the word ‘sad.’

Semantic Search and SEO

Semantic search has profoundly impacted SEO strategies:

1. Focus on Topics, Not Just Keywords: Produce content that will give information on given topics extensively as opposed to using keywords.

2. Optimize for User Intent: Incorporate all types of intent behind the searches concerning your content into your knowledge base.

3. Use Structured Data: Applying schema markup to your pages will help the search engine perceive what the page is all about.

4. Create Natural, Conversational Content: Write in a manner that the answers given are in response to the questions posed by users.

Conclusion

Semantic search is at the forefront of changing the way users search for and engage with content over the internet. With the developments in AI and NLP, search will become ever more natural and customised to the user. With the rise of AI semantic search, companies need to incorporate methodologies embracing semantic search to make their content indexed and easily found.

FAQs

Is Google a semantic search engine?

Yes, Google is a semantic search engine. It can incorporate a much deeper form of analysis based on the user query that does not rely on matching search terms. This helps Google better understand the user’s intent and translate this into matching natural language with probable searches and understanding of synonyms.

What is semantic search in LLM (Large Language Models)?

Semantic search in Large Language Models (LLMs) is the capacity of AI systems to process natural language queries and react based on the actual meanings of the set queries. It does not stop at keywords but enables LLMs to understand concepts that are relevant to the user query and respond with conceptually related pieces of information even if the query words are not used.

What is the difference between semantic search and text search?

Text search is based on the idea of looking for the exact phrase or words within the search corpus or words closely related to it. Traditional search only searches for keywords and does not grasp the context and related terms. It can also bring the desired results regardless of its indexation in the search engine.

What is the difference between lexical search and semantic search?

Lexical search works based on the meaning of individual words that are looked for and very closely resemble the key words to be searched. Whereas, Semantic search looks at the entire question to interpret their intent, accepted meanings, related concepts, and more. It leads to faster and more conceptually related results, and it is easier to use in general.