2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011) 2011
DOI: 10.1109/fuzzy.2011.6007378
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Developing a fuzzy search engine based on fuzzy ontology and semantic search

Abstract: Most of existing search engines retrieve web pages by means of finding exact keywords. Traditional keyword-based search engines suffer several problems. First, synonyms and terms similar to keywords are not taken into consideration to search web pages. Users may need to input several similar keywords individually to complete a search. Second, traditional search engines treat all keywords as the same importance and cannot differentiate the importance of one keyword from that of another. Third, traditional searc… Show more

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Cited by 26 publications
(11 citation statements)
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“…The authors present a technique that collects search terms and their semantic relationship from the documents of some office applications to generate the XML-based search indices that can effectively locate the office documents. Finally and important to this literature review are fuzzy search engines [20] [21] [22].. The authors seem to agree that synonyms and similar keywords are not taken into consideration in traditional searches, users may need several keywords individually to complete a search or even that all keywords are treated with the same importance.…”
Section: Related Concepts Backgroundmentioning
confidence: 99%
“…The authors present a technique that collects search terms and their semantic relationship from the documents of some office applications to generate the XML-based search indices that can effectively locate the office documents. Finally and important to this literature review are fuzzy search engines [20] [21] [22].. The authors seem to agree that synonyms and similar keywords are not taken into consideration in traditional searches, users may need several keywords individually to complete a search or even that all keywords are treated with the same importance.…”
Section: Related Concepts Backgroundmentioning
confidence: 99%
“…We developed a fuzzy search engine for querying the fuzzy ontology. This engine makes queries based on ␣-cuts -a popular search tactic in fuzzy semantic networks [11,12,26]. The proposed search engine consists of five serial components including fuzzy feature extraction, fuzzy query generation, fuzzy query optimization, search engine, and optimal solution selection.…”
Section: Fuzzy Search Enginementioning
confidence: 99%
“…Refer to the formula used in [26], which adopted the mutual information model to implement semantic search in web. The mutual information I(x, y) is defined as…”
Section: Semantic Query Expansionmentioning
confidence: 99%
“…WordNet, EuroWordNet, and some others dynamically constructed the semantic relationship from the document collection by the technologies such as term clustering [22,23], and mutual information model [24][25][26]. Among these technologies, mutual information model is widely used [24,[26][27][28][29].…”
Section: Semantic Query Expansionmentioning
confidence: 99%