RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms

2009 ◽  
pp. 181-192 ◽  
Author(s):  
Alexander Hogenboom ◽  
Viorel Milea ◽  
Flavius Frasincar ◽  
Uzay Kaymak
Keyword(s):  
Genetic Algorithms ◽  
Query Optimization

Query Formation and Information Retrieval with Ontology

2007 ◽  
pp. 310-323
Author(s):  
Sheng-Uei Guan
Keyword(s):  
Genetic Algorithms ◽  
Information Retrieval ◽  
Query Optimization ◽  
Formation Process ◽  
Retrieval System ◽  
Mobile Commerce ◽  
Information Retrieval System ◽  
Optimization Techniques ◽  
Tree Structure ◽  
Retrieval Scheme

This chapter presents an ontology-based query formation and information retrieval system under the mobile commerce (m-commerce) agent framework. A query formation approach that combines the usage of ontology and keywords is implemented. This approach takes advantage of the tree structure in ontology to form queries visually and efficiently. It also uses additional aids such as keywords to complete the query formation process more efficiently. The proposed information retrieval scheme focuses on using genetic algorithms (GAs) to improve computational effectiveness. Other query optimization techniques used include query restructuring by logical terms and numerical constraints replacement.

OntoQuery-An Evolutionary Approach for Query Formation and Information Retrieval

2011 ◽  
pp. 140-160
Author(s):  
Sheng-Uei Guan ◽  
Chang Ching Chng ◽  
Fangming Zhu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Information Retrieval ◽  
Query Optimization ◽  
Formation Process ◽  
Relevance Feedback ◽  
Fitness Function ◽  
Optimization Techniques ◽  
Retrieval Scheme

This chapter proposes the establishment of OntoQuery in an m-commerce agent framework. OntoQuery represents a new query formation approach that combines the usage of ontology and keywords. This approach takes advantage of the tree pathway structure in ontology to form queries visually and efficiently. Also, it uses keywords to complete the query formation process more efficiently. Present query optimization techniques like relevance feedback use expensive iterations. The proposed information retrieval scheme focuses on using genetic algorithms to improve computational effectiveness. Mutations are done on queries formed in the earlier part by replacing terms with synonyms. Query optimization techniques used include query restructuring by logical terms and numerical constraints replacement. Also, the fitness function of the genetic algorithm is defined by three elements, number of documents retrieved, quality of documents, and correlation of queries. The number and quality of documents retrieved give the basic strength of a mutated query.

Applying genetic algorithms to query optimization in document retrieval

2000 ◽  
Vol 36 (5) ◽  
pp. 737-759 ◽  
Author(s):  
Jorng-Tzong Horng ◽  
Ching-Chang Yeh
Keyword(s):  
Genetic Algorithms ◽  
Query Optimization ◽  
Document Retrieval

Query Optimization Using Genetic Algorithms

2010 ◽  
Vol 2 (3) ◽  
pp. 139-144 ◽  
Author(s):  
M. Sinha ◽  
S.V. Chande
Keyword(s):  
Genetic Algorithms ◽  
Query Optimization

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms
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