A search engine may be great at finding content on websites and online documents that contain the words you searched for. Type in ‘elephants’ and you’ll find websites dedicated to these animals. Type in ‘elephant grass’ and you’ll find references to this kind of grass or perhaps either elephants or grass if the search engine can’t find references to both together. In these cases, the search engine offers you sites containing content that refers to either keyword. But type in keywords that can carry different meanings when used in part, such as ‘classes’ and ‘class’ and search engines can be less effective, with detrimental effects on website optimisation.

Why any Search Engine Might Discriminate Concepts to Improve Search Results

The issue lies in the fact that many search engines won’t recognise that the variations of words may be related and will see them as totally different keywords, offering many results for one but considerably less for another. The search engine can’t grasp the semantic concept behind words that are different but related. This is where clustering might help. Clustering results by concept means that the search engine is recognising the semantic meaning behind groups of words and therefore pulls results together even when different variations on words have been inputted. Therefore, in the ‘class’ and ‘classes’ example, the search engine would yield results relating to both words if it recognises that the user may be interested in results relating to both.

When search engines cluster concepts, they analyse how each part of a user’s query might be related and how the most effective results can be produced. A benefit, therefore, would be a literal grouping of search results stemming from a particular query. This would be most useful if you want to search for something with many contexts, all based on the same word or phrase, such as ‘puma’, for example, which could be a big cat or a brand name. A search engine would group results for the various concepts found together using clustering techniques, so you then use the results having first weeded out wholly irrelevant results.

The outcome would be a positively diverse range of results too, something that may be missing when search results are not clustered. Search engines would show a percentage of each concept across the results as a whole. You could identify the clusters each part of your search query appeared in, which might lead to more results based around these concepts.

On a more basic level, the use of concepts by search engines could be a way to identify the meaning of each word and when a word entered during a search query is misspelt. If the search engine can’t find a concept relating to a word, there’s a good chance that word is misspelt and other words could be suggested. Meanwhile, if there is a concept associated with a word, even if at first glance the keyword entered appears to be a misspelling, the search engine would be able to identify this based on the concepts and other keywords associated with the query. The search results displayed would then be relevant to the concept entered and not other concepts that are related to a slightly different spelling.

Ultimately, clustering concepts could lead to greater website SEO, as users are better able to use search engines to find exactly what they’re looking for. The better search engines can understand semantics and recognise the context of queries, the more precise and effective user searches will be.

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