Title
Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm
Abstract
The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a weighted combination of the basic performance criteria, precision and recall.A desirable aspect of IRSs, especially of those based on the IQBE paradigm, is to be able to get more than one query for the same information needs, with high precision arid recall values or with different trade-offs between both.In this contribution, a new IQBE process is proposed combining a previous basic algorithm to automatically derive Boolean queries for Boolean IRSs [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431] and an advanced evolutionary multiobjective approach [Coello, C. A., Van Veldhuizen, D. A., & Lamant, G.B. (2002). Evolutionary algorithms for solving multiobjective problems. Kluwer Academic Publishers], which obtains several queries with a different precision-recall trade-off in a single run. The performance of the new proposal will be tested on the Cranfield and CACM collections and compared to the well-known Smith and Smith's algorithm, showing how it improves the learning of queries and thus it could better assist the user in the query formulation process.
Year
DOI
Venue
2006
10.1016/j.ipm.2005.02.006
Inf. Process. Manage.
Keywords
Field
DocType
query learning,genetic programming,derive boolean query,inductive query by example,text retrieval,derive query,new iqbe process,boolean information retrieval systems,information science,multiobjective evolutionary algorithms,boolean irss,boolean query,relevance feedback,iqbe paradigm,multiobjective iqbe evolutionary algorithm,information retrieval system,evolutionary algorithm,information need,query by example
Relevance feedback,Information retrieval,Evolutionary algorithm,Computer science,Genetic programming,Query by Example,Boolean algebra,Artificial intelligence,Standard Boolean model,Genetic algorithm,Boolean conjunctive query
Journal
Volume
Issue
ISSN
42
3
Information Processing and Management
Citations 
PageRank 
References 
14
0.67
24
Authors
3
Name
Order
Citations
PageRank
O. Cordón1138066.74
Enrique Herrera-Viedma213105642.24
M. Luque3140.67