The Significance of Semantic Search and What it Means For SEO

Jan 15, 2022
6 min read

Data searching techniques have come a long way since the early days of the Internet, to the ubiquitous network that it has become today. Traditionally, searching algorithms are dime a dozen, ranging from binary search, sublist search, linear search, Fibonacci search, exponential search, and more. Search engines essentially use the same techniques for advanced algorithms that analyze user search queries to help them navigate the Internet.

Back in the day, search engines were pretty much rhetorical; that is to say, Google, Bing, and others looked for words that matched the literal words of the search query, and presented links that contained that exact statement, without taking into consideration, what the meaning of the query might be. For some simple queries, this kinda worked; for complex ones, the search results returned links that in some cases, made no sense at all. To that end, the concept of semantic search came into existence – essentially, it solved the basic pain point of lexical search, i.e., searching with meaning.

‘Semantics’ basically signifies ‘meaning’ or ‘essence’ of something. Conventionally, it is a branch of linguistics focusing on the various meanings of words, their logic, and their symbolic use. In the digital world, semantic search essentially is a deep-rooted data searching technique wherein a search query does more than aiming to find links with similar words – it helps to understand the contextual meaning of the words typed and return the most relevant links. This technique has become wildly popular in recent times, given that it makes browsing more inclusive, meaningful, and complete.

In laymen terms, semantic search tries to understand exactly what the user is asking, thereby enhancing search accuracy to a great extent, as opposed to merely matching keywords to pages. It deploys synonym usage, concept matching, and natural language algorithms to provide interactive, useful results. Searcher intent is a vital part of online search, and semantic searching techniques helps to bring about a better understanding of the same, in addition to enhancing the ability to extract near perfect answers and delivering customized results.

How does Semantic Search Work?

Semantic search functions basically on analyzing user intent – that can depend on numerous factors, such as the user’s search history, location, search queries, and more. Analyzing the intent helps create links between the words in the search query, thereby making the search more personalized and relatable to the user.

Context plays a vital role in human communication. What we speak or how we react isn’t just based on that precise moment, but on what has been previously said. The time of the conversation, the circumstances, background knowledge, and the kind of relationship people in a conversation share have a great deal to do with what has been said or might be said. This kind of understanding is exactly what semantic search techniques attempt to emulate. Therefore, it uses specific algorithms with intricate learning patterns to mimic human behavior and thinking to minimize the transition between how users interact with people and the way they interact with search results.

In essence, semantic search works by adding a major level of human understanding to search queries; however, the techniques need periodic upgradation of learning patterns and behavior to return the more relevant results. Parameters such as conversion rates, bounce rates, and more, can help semantic search algorithms enhance user understanding and experience. It is very much like machine learning, in the sense that it uses past history to determine the present requirement to improve experience.

Why is Semantic Search Important?

Similar to humans use various languages, tones, and ways to convey something, online users have different methods to enumerate what they want. Now, these queries can be vastly different and ambiguous, therefore it is essential that search algorithms understand the relationships between words. For instance, when a user types something like ‘Top 10 books to read before 2021 ends’, Google ensures to return the most accurate and well-searched, well-ranked articles.

Semantic search is important because it is what helps to accrue a maximum amount of traffic and leads.  Especially for business owners, it is vital to ensure that the experience they provide their customers is efficient and easy to navigate. In eCommerce for example, semantic search techniques will help users easily shop on the site and encourage them to come back. Semantic search helps improve the efficacy of websites, making the user’s digital experience seamless and appealing. The more relevant a particular search is, the more impact it has on the site performance, in terms of measurable metrics.

As search engines continue to understand natural language by scrutinizing search intent, analyzing the relationship between the entered words, and cross referencing he context, semantic search will make it possible for them to distinguish between various queries and interpret search intent by considering parameters such as spellings and search history.

How Does Semantic Search Help in SEO?

The significance of SEO has majorly escalated with digitalization becoming commonplace. A lot has changed since the last decade and a half – at the time, the only task SEO experts were concerned about, was to direct traffic to sites by getting as many backlinks as possible with the inclusion of common keywords. Now however, the focus has considerably changed, and there’s a lot of importance attached to user intent, behavior, and context, making semantic search techniques rather vital for SEO. Identifying keywords alone is no longer the goal – search engines need to understand what these words mean and provide relatable content that aligns with the search queries. Here’s where semantic search comes in.

Semantic search helps a search engine come up with the most accurate SERP results based on query context and user intent. This enables search engines such as Google to deliver an enhanced experience to its users by providing high-ranking, high-quality, and relatable content to its users. In today’s scenario, semantic search is all the more vital for SEO, for the following reasons:

· It has encouraged the elimination of keywords and prioritized topics

As semantic search takes a bigger dive in SEO, experts have come to realize that while keywords are important, topics are now gaining more precedence. Since search engines work to deliver the most significant, relatable content, it has to be comprehensive, all-inclusive, and more informative than ever before. Semantic search will help SEO in this regard, as it will help create information pages on the entire topic, on which optimization practices can be conducted to ensure that the content is optimized for users and search engines.

· It has pushed the inclusion of related keywords

Semantic search can be enhanced by adding extra keywords, or more specifically LSI keywords – which essentially are words that are closely connected to the content on the site. Latent Semantic Indexing keywords help provide context to content, thereby fulfilling the premise of semantic searches. They also help to better understand what the content says and how it will help users.

· It has encouraged content optimization

Optimized content always works well with the audience, and search engines prefer to deliver appealing, high-quality results directly on SERPs. For enhanced search visibility, content optimization should be a priority – like featuring tables, paragraphs, lists, and more. Targeting long-tail keywords and deploying good formatting makes content look appealing enough for featured snippets.

· It has reiterated the significance of search intent

As mentioned earlier, search intent is one of the main benefits of semantic search. Every type of keyword – informational, transactional, and navigational has a different intent. Knowing user intent beforehand will help chart out suitable phrases and in developing content ideas for the website.

· It falls in line with the current trend of voice searches

Search queries have now gone conversational – which reiterates the significance of long-tail keywords. No longer do people have the patience to type out longer queries, which has resulted in a substantial increase in voice web searches. To that end, content must be optimized in a way that there is one answer that exactly responds to a common query. Semantics come in after that – where related keywords and topic subjects are included in the rest of the content to help search engines understand which result to deliver in case of a particular query.

Google has always striven to deliver best-quality content to users. At the onset of the time when using conventional keywords was becoming monotonous and unhelpful, Google came up with the Knowledge Graph in 2012 – its first attempt at preferring content context over merely keywords. The Knowledge Graph pioneered the change in search algorithms that got better through the years.

The next year, in 2013, Google rolled out an update of the Hummingbird, which is considered the beginning of the semantic search era. Hummingbird uses natural language processing to make sure that page results match the meaning of what is asked and not just the words.

Two years later, Google introduced RankBrain – an ML system that functions as a smart query analysis AI and is a ranking factor as well. Similar to Hummingbird, it aims to understand search intent. Sometimes however, it may deliver a result for a search query even when the exact words may be lacking in the query.

In 2019, Google launched BERT (Bidirectional Encoder Representations from Transformers) that attempts to further understand search intent and context. BERT helps users to find highly accurate information easily. It also helped analyze long-tail queries and ensure that the content addresses users’ questions.

Google designs all its algorithms keeping in mind what users are looking for online. It will continue to enhance its algorithm updates year after year so as to improve SERPs and ensure that they are accurate enough for individual users. A lot of this can be attributed to the phenomenon of semantic search, because of which Google, as a search engine has been able to form a better understanding of user intent. All said and done, search engines like Google, now and in the future, essentially want to generate results that are not only informative but appropriate and relevant too.