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Semantic search: the next big thing in search engine technology
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Semantic search: the next big thing in search engine technology

Have you ever thought about Google’s uncanny ability to finish your sentences when you’re typing in a Google search query? Kinda creepy. Or the fact that Google (or Bing or Yahoo, among other search engines) can expertly understand a sentence’s meaning and answer very specific questions in its featured snippets (such as in the English language, “How does Queen Elizabeth take her tea?” Or “How old is Harry Potter?”)

Wow. It seems as though something magical helps Google “think” and use psychic abilities. But really, this phenomenon is just the result of Google having a very sophisticated AI-powered search engine that uses semantic search to provide answers. 

What is semantic search? 

If you’re trying to grasp what general semantics is, think about the word “semantic.” In language, the definition of semantics is related to linguistic meaning. So in terms of search, a semantic search engine is doing a study of meaning, focusing on the meanings of search terms being entered. Essentially, semantic search works by drawing links between words and phrases; it’s able to interpret digital content in a more “human” way. When that’s achieved, it can offer the searcher more-personalized and accurate search results.

Examples of semantics

Let’s say you’re getting married and you do a voice search on the English phrase in the lexicon of a bride to be: “dream wedding dress”. A semantics-driven search engine would understand that by “dream”, when it’s linked to “wedding dress”, you mean the synonym “ideal”. 

By contrast, a traditional search engine might be confused by the word “dream” and offer a less accurate set of items on the search engine results page (SERP). What does it mean if you dream about wearing a wedding dress? Um, no.

A semantic search engine would probably present as search results wedding-dress styles that it “thinks” would look dreamy on you (maybe retailers’ currently popular styles). 

What were search engines doing before?

For the past decade or so, search engine technology has relied strictly on keywords themselves in order to interpret the meaning of content. An article on the Web about wedding dresses, for example, would only have been picked up by web crawlers if keywords like “wedding dress” or “vintage wedding dress” were used continually throughout the content. This meant that digital content marketers and authors had to make sure their keywords matched what people were entering in search boxes.

The art and science of getting a web crawler to take notice of your web content’s keywords and thereby highly rank your article in search results is called search engine optimization (SEO). Creating optimized content translates to more people being likely to see it appear at the top of their search results, where there’s a good chance they’ll click on the link and read the content. 

Why have we needed SEO? 

Optimizing content for the Web has been necessary because search engine technology has not been advanced enough to decipher the meaning of human-generated content as intended. Semantic search is a big leap forward because it supplies more of a focus on searcher intent, contextual meaning from a linguistic standpoint, and sophisticated understanding of the relationships between words.

Digital marketers might not want to throw out their SEO strategy, keyword research, or lists of ranking SEO terms just yet, but if search engine technology advances enough, in theory, the “intelligence” of semantic search will substantively change how we produce content for the Web, and traditional keywords could become irrelevant.

For the moment, however, we aren’t there. Until things progress further, marketing content creators are advised to take a topic-driven approach to producing web content. Rather than aiming to rank content for one or two main keywords, you should be ensuring that you cover a topic in depth and use several ranking long-tail keywords focused on user intent, rather than one or two shorter, general ones that will have a tough time ranking as well in the competitive SEO-word landscape.  

The principles of semantic search 

Semantic search is governed by two principles: search intent and semantic meaning. To interpret natural language more accurately, or contextually, search engines must decipher content based on both of these factors. 

Search intent 

Search intent refers to the intention or motivation of the person doing the search. When you type “buy soap” in a search bar, that should be straightforward enough for the search engine to understand.

But people don’t always query search engines in the most straightforward way. In this instance, for example, they might enter “need soap now” or “cheap soap” or “nice smelling soap bar”. From the various phrases in user searches, the search engine must figure out whether they want to buy something, and if so, what, exactly; or whether they simply want information about it (e.g., what’s the cheapest soap on the market?). 

A semantic search engine is better equipped to interpret the meaning of a word. It can better understand query intent, and as a result, it can generate search results that are more relevant to the searcher than what a traditional search engine might display. 

Semantic meaning 

Semantics is a branch of linguistics focused on meaning. It is about the relationships between words, rather than the words in isolation. While a traditional search engine deciphers queries based on keywords or short strings of words, a semantic search engine takes a more holistic approach, considering what the words mean and how they relate to one another. This process is more similar to the way humans interpret language. Ideally, semantic search means no more irrelevant search results, which, in turn, means a better user experience.  

Why do we need semantic search? 

Have you ever had to click through multiple search results pages looking for the information you needed? Or been served up seemingly random, unrelated search results and had to try your search all over again with different words?

This might have been the result of your entering a poorly worded query, but it’s also possible that a traditional search engine was having a bad day searching the knowledge base and misunderstood your request.

Why has traditional search engine technology at times failed to turn up the desired results? 

The ability to rank has been easy to manipulate 

Starting in the ’90s, the appearance of keywords and number of times they were used in content determined the ranking designated by search engines. A web page that used a keyword the most times would appear first in search results. When marketers realized how ranking worked, they or their web developers began “keyword stuffing” their content in order to have it rank higher. 

Traditional search has taken users to low-quality content  

Because marketers and developers were doing so much keyword stuffing, they were degrading the quality of content. The Web was deluged with low-quality content targeted for crawlers, rather than well-crafted, enjoyable-to-read content created for people. Articles were appearing online that had plenty of ranking keywords but were poorly written or didn’t contain the right information.

For example, an article that repeated the term “red panda extinction” but didn’t cover how red pandas were becoming extinct or provide any relevant statistics about the phenomenon wasn’t a great resource for readers interested in that topic.  

People searching have had to work harder 

Earlier search engines struggled to properly decipher the content of queries and would typically misinterpret people’s requests. They could produce search results only when the exact phrase matched, so sometimes users had to describe what they wanted in various ways, and to try searches multiple times before they got a match.

By contrast, these days, advanced search engines with technology like Google’s algorithm can expertly decipher almost any query you throw at them — poor spelling and weird phrasing won’t deter them. Why? Because they’re utilizing semantic search technology. 

Why do ecommerce sites and marketplaces need semantic search engines?

Thanks to heavy use of Google, on individual companies’ websites, most visitors expect to be provided search bars that perform like Google’s, generating relevant results the first time and intuitively interpreting the correct meaning of their queries. But many companies hosting online platforms and digital marketplaces underestimate the value of a semantic search engine and so they haven’t invested in semantic search.

A search experience that doesn’t meet people’s basic expectations is obviously disappointing, but that’s just the tip of the iceberg. If someone visits a company’s website only to get stuck using outdated search technology, they’ll rate it as a bad experience by comparison. They may also not come back to the site, and possibly leave negative feedback about it on social media, leading to potentially diminished revenue for the company. So it’s important that business executives running online platforms recognize this stumbling block and take the necessary steps to provide their users or customers with modern search experiences and rich results.  

Provide great semantic search with the Algolia search API 

Algolia uses powerful machine learning–driven semantic search to generate rewarding online experiences for companies’ website visitors and shoppers.

Algolia Answers finds details deep within material to produce more-accurate search results. Its artificial intelligence–powered search provides humanlike understanding of query text and ensures that people can intuitively navigate a website or online marketplace. On ecommerce platforms, it can reduce the amount of time that elapses between when someone does a search for an item and when they make a purchase, fast-tracking conversion.

To explore the power of semantic search and reboot your search engine, get in touch with us or start building for free.

About the author
Vincent Caruana

Sr. SEO Web Digital Marketing Manager

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