Intelligent Semantic Web Search Engines: A Brief Survey <s> Current Web & Limitations <s> This paper investigates the semantic search performance of search engines. Initially, three keyword-based search engines (Google, Yahoo and Msn) and a semantic search engine (Hakia) were selected. Then, ten queries, from various topics, and four phrases, having different syntax but similar meanings, were determined. After each query was run on each search engine; and each phrase containing a query was run on the semantic search engine, the first twenty documents on each retrieval output was classified as being “relevant” or “non-relevant”. Afterwards, precision and normalized recall ratios were calculated at various cut-off points to evaluate keyword-based search engines and the semantic search engine. Overall, Yahoo showed the best performance in terms of precision ratio, whereas Google turned-out to be the best search engine in terms of normalized recall ratio. However, it was found that semantic search performance of search engines was low for both keyword-based search engines and the semantic search engine. <s> BIB001 </s> Intelligent Semantic Web Search Engines: A Brief Survey <s> Current Web & Limitations <s> BackgroundCurrent search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured statements over which they can reason.ResultsHere, we introduce a third approach, GoWeb, which combines classical keyword-based Web search with text-mining and ontologies to navigate large results sets and facilitate question answering. We evaluate GoWeb on three benchmarks of questions on genes and functions, on symptoms and diseases, and on proteins and diseases. The first benchmark is based on the BioCreAtivE 1 Task 2 and links 457 gene names with 1352 functions. GoWeb finds 58% of the functional GeneOntology annotations. The second benchmark is based on 26 case reports and links symptoms with diseases. GoWeb achieves 77% success rate improving an existing approach by nearly 20%. The third benchmark is based on 28 questions in the TREC genomics challenge and links proteins to diseases. GoWeb achieves a success rate of 79%.ConclusionGoWeb's combination of classical Web search with text-mining and ontologies is a first step towards answering questions in the biomedical domain. GoWeb is online at: http://www.gopubmed.org/goweb <s> BIB002
Present World Wide Web is the longest global database that lacks the existence of a semantic structure and hence it becomes difficult for the machine to understand the information provided by the user in the form of search strings. As for results, the search engines return the ambiguous or partially ambiguous result data set; Semantic web is being to be developed to overcome the following problems for current web. • The web content lacks a proper structure regarding the representation of information. • Ambiguity of information resulting from poor interconnection of information. • Automatic information transfer is lacking. • Usability to deal with enormous number of users and content ensuring trust at all levels. • Incapability of machines to understand the provided information due to lack of a universal format. Hakia BIB001 is a general purpose semantic search engine that search structured text like Wikipedia. Hakia calls itself a "meaning-based (semantic) search engine"  . They're trying to provide search results based on meaning match, rather than by the popularity of search terms. The presented news, Blogs, Credible, and galleries are processed by hakia's proprietary core semantic technology called QDEXing BIB001 . It can process any kind of digital artifact by its Semantic Rank technology using third party API feeds BIB002 .