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Semantic search is search that understands the meaning of a natural language query as opposed to viewing search terms as keywords. ExamplePeople commonly enter natural language searches such as "how did Tokyo become a megacity?" A semantic search would interpret this term beyond simplistic analysis of keywords such as Tokyo and megacity. It would understand that the user is looking for information about the history of Tokyo and how its population became so large. For example, it wouldn't return a link to a sushi restaurant in Chicago called "Tokyo megacity sushi."
TechnologySemantic search requires a search engine backend that can discover, rank and index knowledge resources. It also requires natural language processing capabilities. Natural language processing is typically based on artificial intelligence as machine learning is required to tackle the complexity of natural languages such as English.
ValueSemantic search is potentially more powerful than other search techniques such as contextual search and keyword search. Humans are good at framing complex questions. Search tools could potentially answer extremely complex queries such as "What was that sushi restaurant in Chicago that was popular back in the 1990s but it closed down for a few years and then reopened with the same name and owner in the suburbs?"
Artificial Intelligence
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