Browse wiki

Jump to: navigation, search
Expanding approach to information retrieval using semantic similarity analysis based on wordnet and wikipedia
Abstract Performance of information retrieval (IR) Performance of information retrieval (IR) systems greatly relies on textual keywords and retrieval documents. Inaccurate and incomplete retrieval results are always induced by query drift and ignorance of semantic relationship among terms. Expanding retrieval approach attempts to incorporate expansion terms into original query, such as unexplored words combing from pseudo-relevance feedback (PRF) or relevance feedback documents semantic words extracting from external corpus etc. In this paper a semantic analysis-based query expansion method for information retrieval using WordNet and Wikipedia as corpus are proposed. We derive semantic-related words from human knowledge repositories such as WordNet and Wikipedia, which are combined with words filtered by semantic mining from PRF document. Our approach automatically generates new semantic-based query from original query of IR. Experimental results on TREC datasets and Google search engine show that performance of information retrieval can be significantly improved using proposed method over previous results.ing proposed method over previous results.
Abstractsub Performance of information retrieval (IR) Performance of information retrieval (IR) systems greatly relies on textual keywords and retrieval documents. Inaccurate and incomplete retrieval results are always induced by query drift and ignorance of semantic relationship among terms. Expanding retrieval approach attempts to incorporate expansion terms into original query, such as unexplored words combing from pseudo-relevance feedback (PRF) or relevance feedback documents semantic words extracting from external corpus etc. In this paper a semantic analysis-based query expansion method for information retrieval using WordNet and Wikipedia as corpus are proposed. We derive semantic-related words from human knowledge repositories such as WordNet and Wikipedia, which are combined with words filtered by semantic mining from PRF document. Our approach automatically generates new semantic-based query from original query of IR. Experimental results on TREC datasets and Google search engine show that performance of information retrieval can be significantly improved using proposed method over previous results.ing proposed method over previous results.
Bibtextype article  +
Doi 10.1142/S0218194012500088  +
Has author Fei Zhao + , Fang F. + , Yan F. + , Jin H. + , Zhang Q. +
Has extra keyword Dataset + , Google search engine + , Human knowledge + , Pseudo relevance feedback + , Pseudo-relevance feedbacks + , Query expansion + , Relevance feedback + , Semantic relationships + , Semantic similarity + , Wikipedia + , Wordnet + , Natural language processing systems + , Ontology + , Search engine + , Semantics + , Websites + , Information retrieval +
Has keyword Information retrieval + , Pseudo-relevance feedback + , Query expansion + , Semantic similarity +
Issn 2181940  +
Issue 2  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 305–322  +
Published in International Journal of Software Engineering and Knowledge Engineering +
Title Expanding approach to information retrieval using semantic similarity analysis based on wordnet and wikipedia +
Type journal article  +
Volume 22  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 7 November 2014 19:11:27  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 19:11:27  +
DateThis property is a special property in this wiki. 2012  +
hide properties that link here 
Expanding approach to information retrieval using semantic similarity analysis based on wordnet and wikipedia + Title
 

 

Enter the name of the page to start browsing from.