Early information retrieval systems were fairly basic programs designed to essentially to find a match between search terms in a query and those same words appearing in documents. As search has evolved, however, simple keyword-based matching is only one of dozens of factors used to find relevant documents related to a searcher's query.
Because searchers are often parsimonious with query terms, search engines need to develop contextual models to help them better understand both queries and web documents. Search optimizers can leverage this by optimizing not just isolated keywords, but keyword clusters that offer a richer context of meaning for a search engine to chew on.
Today's Search Illustrated graphic depicts this approach:
Early information retrieval systems were fairly basic programs designed to essentially to find a match between search terms in a query and those same words appearing in documents. As search has evolved, however, simple keyword-based matching is only one of dozens of factors used to find relevant documents related to a searcher’s query.
Because searchers are often parsimonious with query terms, search engines need to develop contextual models to help them better understand both queries and web documents. Search optimizers can leverage this by optimizing not just isolated keywords, but keyword clusters that offer a richer context of meaning for a search engine to chew on.
Today’s Search Illustrated graphic depicts this approach:
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