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Semantic Indexing: How AI and Machine Learning Will Lead to More Efficient Internet Searches

April 6, 2023

(Based on the Article: Machine Learning Applied to Media Libraries for Insights, Search, and Segmentation)

Whether it’s for academic research or videos of cats, billions of people search the web on a daily basis. Technology used for Internet searches have changed a lot in the last 20 years, making it easier to find the content consumers need and crave. For example, semantic searches have changed the game when it comes to surfing the web. This technology has been blossoming over the last 15 years, and has helped create a new system that will revolutionize the world of web searches: Semantic Indexing.

Surfing the web hasn’t always been the walk in the park that it is today. In the early days, search engines used a technique called “lexical searching.” This system involved engines seeking literal matches of query words without understanding the query itself. For example, if someone searched for “cat afraid of cucumber video,” a lexical search would show results for the words “cat,” “afraid,” “of,” “cucumber,” and “video.” This system might lead to the specific video being sought out, but it’s much more likely that someone will have to deal with separate articles, images, or videos about cats and cucumbers.

Starting in the 2010’s, however, lexical searching was being pushed to the side to make room for semantic searching, which is described as, “searches with meaning.” Semantic searching adds the context of a query into the search, so finding videos of cats that are afraid of cucumbers becomes way easier since the search engine now knows exactly what a person is asking. Still, search engines can go further. An emerging method of searching, known as “Semantic Indexing,” will soon revolutionize how people find and consume content on the internet.

Semantic Indexing uses AI and Machine Learning to search the web for media, even if the query is unstructured. This means that someone could type, “cat scared cucumber” and get the same, if not better results, as a more structured query in a semantic search engine. Using this technology, media can even be found if the query uses relevant synonyms and descriptions, meaning one could search, “feline afraid of vegetable” and still get the videos they are searching for.

All this is achieved thanks to the AI systems being used for these searches. OpenAi and the Elasticsearch database, for example, are some of the companies that provide the infrastructure to make this possible. Their programs learn, not only which topics are the most relevant in a search, but how each word interacts with one another. This is how it can understand the context of a sentence. This system can even work in a multilingual context!

Search engines are currently in an AI race, adding innovative new technologies and systems to improve the web searching experience. Most of them will likely move to a Semantic Indexing system thanks to its flexibility, ingenuity, and ability to improve over time. There’s still a lot to figure out with AI and Machine Learning, but this is just one of many applications that these amazing technologies are capable of.

Tag(s): AI

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