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Automated Query Learning with Wikipedia and Genetic Programming
Abstract Most of the existing information retrievalMost of the existing information retrieval systems are based on bag of words model and are not equipped with common world knowledge. Work has been done towards improving the efficiency of such systems by using intelligent algorithms to generate search queries, however, not much research has been done in the direction of incorporating human-and-society level knowledge in the queries. This paper is one of the first attempts where such information is incorporated into the search queries using Wikipedia semantics. The paper presents an essential shift from conventional token based queries to concept based queries, leading to an enhanced efficiency of information retrieval systems. To efficiently handle the automated query learning problem, we propose Wikipedia-based Evolutionary Semantics (Wiki-ES) framework where concept based queries are learnt using a co-evolving evolutionary procedure. Learning concept based queries using an intelligent evolutionary procedure yields significant improvement in performance which is shown through an extensive study using Reuters newswire documents. Comparison of the proposed framework is performed with other information retrieval systems. Concept based approach has also been implemented on other information retrieval systems to justify the effectiveness of a transition from token based queries to concept based queries.en based queries to concept based queries.
Abstractsub Most of the existing information retrievalMost of the existing information retrieval systems are based on bag of words model and are not equipped with common world knowledge. Work has been done towards improving the efficiency of such systems by using intelligent algorithms to generate search queries, however, not much research has been done in the direction of incorporating human-and-society level knowledge in the queries. This paper is one of the first attempts where such information is incorporated into the search queries using Wikipedia semantics. The paper presents an essential shift from conventional token based queries to concept based queries, leading to an enhanced efficiency of information retrieval systems. To efficiently handle the automated query learning problem, we propose Wikipedia-based Evolutionary Semantics (Wiki-ES) framework where concept based queries are learnt using a co-evolving evolutionary procedure. Learning concept based queries using an intelligent evolutionary procedure yields significant improvement in performance which is shown through an extensive study using Reuters newswire documents. Comparison of the proposed framework is performed with other information retrieval systems. Concept based approach has also been implemented on other information retrieval systems to justify the effectiveness of a transition from token based queries to concept based queries.en based queries to concept based queries.
Bibtextype misc  +
Has author Pekka Malo + , Pyry Siitari + , Ankur Sinha +
Has remote mirror http://arxiv.org/pdf/1012.0841v1  +
Language English +
Number of citations by publication 1  +
Number of references by publication 0  +
Title Automated Query Learning with Wikipedia and Genetic Programming +
Type unknown  +
Year 2010 +
Creation dateThis property is a special property in this wiki. 30 July 2011 11:15:43  +
Categories Publications without published in parameter  + , Publications without keywords parameter  + , Publications without license parameter  + , Publications without DOI parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 11 February 2012 00:33:44  +
DateThis property is a special property in this wiki. 2010  +
hide properties that link here 
Semantic Content Filtering with Wikipedia and Ontologies + Has reference
Automated Query Learning with Wikipedia and Genetic Programming + Title
 

 

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