How to handle Synonyms in AI-based Search Engines?

As modern society becomes increasingly reliant on the internet for information and communication, search engines have become integral to the way we navigate the vast expanse of the web. 

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Whether we are looking for a specific piece of information, trying to find a product to purchase, or simply seeking entertainment, search engines are the go-to tool for finding what we need. Having an excellent search engine is a baseline for modern businesses in any digital facing industry – but 42% of websites fail when dealing with simple queries.

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However, handling synonyms can be a challenge. In this article, we will explore how synonyms were usually handled before, how modern search engines handle them, and what that means for you.

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To understand why AI-based engines are changing the search landscape, it's important to first understand how average users make searches and how those searches are then handled under string-based search engines.

How do users make searches? 

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If every user knew exactly what they were looking for, search engines would be fairly simple platforms. But this is never the case. 

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How many times do you find yourself looking for something you can’t quite remember the name of? Or the last time you looked up a song you only remember the tune to? 

Googles engine could find the exact item even with a vague and misspelled query. 

Can yours?

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Advanced search engines are even able to handle multi language searches:

Iewa is a Yoruba term for beautiful

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But, even Google falters when it comes to certain queries:

‘Fire’ is a slang term denoting something that is awesome or cool

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An advanced search engine like Google can usually handle such complex searches. But most websites or businesses don’t feature search engines with such advanced features. 

Why is that an issue?

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Imagine you want to find a recipe for a chocolate cake, but the search engine only returns results for "chocolate cake" and not "devil's food cake" or "dark truffle lava cake." Nothing's more frustrating than knowing what you want and still not being able to find it.

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This is where synonyms – words that have the same or similar meaning – become important in search engines. 

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Handling synonyms is incredibly important – even people who share the same language might have dozens of terms for the same thing. 

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Languages keep on evolving, and newer terms keep cropping up, so it's important for search engines to be able to adapt and learn new synonyms as they arise in order to accurately understand and process language.

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Next, we’ll take a look at older methods of handling synonyms and how modern search engines make the entire process significantly easier.

How string-based engines handle synonyms

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When a user enters a search query into a search engine, the system uses algorithms to scour the vast expanse of the internet and return a list of relevant results. 

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With traditional string-based matching, a business or developer would have to manually create a database of synonyms for each word or phrase that might be used in a search query. This means that if a search query contains a synonym that is not mapped in the database, the search engine will not return relevant results.

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This ‘database’ or synonym map is usually creating by using a digital thesaurus or repository to expand a user's search query to include synonyms. For example, if a user searches for "happy," the search engine could also return results for "content," "pleased," and "joyful."

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This approach can be time-consuming and error-prone, as it requires constant maintenance and updates to ensure that all relevant synonyms are included.

How AI-based search engines handle synonyms

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Artificial intelligence (AI) engines are typically trained on massive datasets in order to learn patterns and relationships that can be used to make predictions or decisions. 

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For example, if an AI engine is being trained to recognize objects in images, it might be fed a dataset of images that are labeled with the objects they depict. The AI engine will analyze these images and their labels, learning to recognize the patterns and characteristics that are associated with different objects. Once the AI engine has been trained on this dataset, it can then be used to identify objects in new, unseen images. 

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Have you ever solved a CAPTCHA? In essence that’s what AI or machine learning training consists of – feeding the same thing again and again and again – until the AI can recognise common characteristics on it’s own and begins to recognise and categorise objects.

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What this means is – even if a search query contains a word that doesn’t exist in the synonym database, the AI will still be able to make connections, and provide relevant results.

How? – The answer is semantic search

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Semantic search is based on the concept of natural language processing (NLP) that aims to understand the meaning and context behind a user's search query. One of the key elements of semantic search is the ability to handle synonyms, or words that have similar meanings.

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For example, consider a user who searches for "best place to eat in New York City." A semantic search engine would understand that the user is looking for recommendations for restaurants in New York City, and might return results for "top restaurants in NYC," "best places to eat in Manhattan," and "favorite dining spots in the Big Apple," even though these phrases do not exactly match the user's search query.

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A large part of semantic search involves query expansion – deciphering and reformulating the query in order to provide more comprehensive search results.

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This can be done through the use of techniques such as word embeddings, which represent words as vectors in a high-dimensional space and can capture the relationships between different words.

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So instead of your developers having to create a synonyms database, the engine comes with its own in-built database – or more accurately a system that can automatically match synonyms – that constantly updates itself.

What does this mean for you? 

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In summary, 'old school' manual synonym matching is no longer necessary with advanced search engines.

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AI-powered engines like Zevi are able to harness the power of NLP, to automatically identify synonyms and related terms resulting in more efficient and accurate search results for your users.