Semantic search is a method of searching for information on the internet in which the search engine uses natural language processing to understand the intent behind a user’s query rather than simply matching keywords. This allows the search engine to return results that are more relevant to the user’s true intent than just the exact keywords they used in their query. With semantic search, the system tries to understand the precise meaning and context of the searched words.
‘…semantic search seeks to understand natural language the way a human would.’
In traditional keyword-based search, the search engine looks for pages that contain the exact keywords specified in the user’s query and returns the results in order of how many times those keywords appear on the page. However, this can lead to many irrelevant or low-quality results, especially for complex or nuanced queries.
With semantic search, the search engine uses natural language processing (NLP) to understand the meaning and intent behind the user’s query. This allows it to return results that are more relevant to what the user is actually looking for, rather than just pages that happen to contain the keywords they used.
For example, a traditional keyword-based search for “best pizza delivery” would return pages that contain those exact words, regardless of whether they’re actually talking about the best pizza delivery places. A semantic search, on the other hand, would use NLP to understand that the user is looking for the top-rated pizza delivery places and would return results that are actually relevant to that query.
It may also factor in the location, history, and preferences to personalise the results. It also tries to understand the relation between the words in the query and gives more weight to the terms which are more relevant; for example, if “Chicago” is mentioned in the query, it will try to look for the best delivery places in Chicago.
To achieve this, semantic search relies on a variety of techniques, such as synonym matching, entity recognition, and sentiment analysis. The goal is to understand the intent behind the query rather than just matching keywords. ‘Around 40% of English words are polysemous—they have two or more meanings.’
Is semantic search a critical part of SEO?
Semantic search can be an essential part of SEO (search engine optimisation) because it can help improve the relevance and quality of the search results returned for a particular query. By understanding the intent and meaning behind a query, a search engine can return results that are more likely to be what the user is actually looking for, leading to a better overall user experience.
As a result, it’s important for website owners and content creators to consider how their pages will be understood by a semantic search engine and to optimise their content accordingly. This can include using semantic keywords and phrases, structuring content in a way that makes it easy for search engines to understand its topic and purpose, and ensuring that the content is of high quality and relevant to the user’s query.
Search engines are using semantic techniques more and more to understand the context of the query, so as a result, semantic SEO is becoming more important to ensure that your website can rank well and be found by the right people.
To optimise your website for semantic search, you can:
- include a mix of different types of content like blogs, videos, images and infographics to make your website more informative.
- use structured data
- include links – both internal and external hyperlinks to give context to what you’re writing about
- utilise headings and subheadings
- answer questions through your blog posts and topics and
- create a clear information architecture.
It’s important to note that while semantic search can be a valuable part of SEO, it’s just one of many factors that search engines use to rank pages and is not the only way to achieve high rankings.
How important is semantic search?
Semantic search is becoming increasingly important for a few reasons:
- It improves the relevance of search results: By understanding the intent and meaning behind a query, a search engine can return results that are more likely to be what the user is actually looking for. This can lead to a better overall user experience and a higher likelihood of the user finding what they’re looking for.
- It allows for more natural language queries: Traditional keyword-based search can be restrictive, as users need to know the exact keywords to use in order to get the results they’re looking for. With semantic search, users can use more natural language queries, which makes the search process more intuitive and easier to use.
- It helps to understand the intent behind a query: Search engines also use querys’ context and background, such as location, history, and personal preferences, to understand the meaning behind a query. This helps search engines to return more accurate results and improve the relevance of the search results,
- It helps with voice search: With the rise of virtual assistants and smart speakers, more and more people are using voice commands to interact with search engines. Semantic search makes it easier for these devices to understand natural language queries and return relevant results.
- It helps with personalisation: Understanding the intent behind a query and users’ context can also be used to personalise the search results, leading to a better overall user experience.
All these factors are making semantic search an important part of search engine algorithms, and websites should strive to optimise for it to ensure that their pages can be found by the right people and rank well in search results.
It’s important to note that semantic search is not the only way for search engines to understand the meaning and context of a query, and it’s just one of many factors that search engines use to rank pages. However, it’s becoming an increasingly important aspect of SEO, and website owners should consider optimising their content for semantic search in order to improve their visibility in search results.
