The current generation of search engines is severely limited in its understanding of the user's intent and the web's content. Find out how semantic search can address these limitations.
by Peter Mika
July 18, 2008
emantic search has attracted a lot of attention in the past year, largely due to the growth of the semantic web as a whole. The term semantic search itself is popular enough to be considered overused. The term refers to searching large semantic web datasets, which is a typical problem for semantic web search engines such as Swoogle, Sindice, SWSE, Falcon-S, and Watson. The term also refers to methods of searching web documents beyond the syntactic level of matching keywords. This article discusses semantic search in this second sense.
The current generation of search engines is severely limited in its understanding of the user's intent—and the
web's content—and consequently in matching the needs for information with the vast supply of resources on the web. For Information Retrieval (IR) purposes, both queries and documents are typically treated at a word or gram level, with minimal language processing involved. The search engine is missing a semantic-level understanding of the query or the content and can only understand the content of a document by picking out the most commonly occurring or underlined words.
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emantic search has attracted a lot of attention in the past year, largely due to the growth of the semantic web as a whole. The term semantic search itself is popular enough to be considered overused. The term refers to searching large semantic web datasets, which is a typical problem for semantic web search engines such as 
