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.

Personalization Information Retrieval Based on Topic Directory

2013 ◽  
Vol 712-715 ◽  
pp. 2659-2663
Author(s):  
Yang Xin Yu ◽  
Yi Zhou Zhang
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Retrieval System ◽  
User Profile ◽  
Information Retrieval System ◽  
Tree Structure ◽  
Experimental Results ◽  
User Profiles ◽  
User Interests ◽  
Computing Process

Personalization information retrieval is very useful in information retrieval system, the user profile can be used to represent the favorites or interests of user. This paper introduces how to automatically learn user interests, build user profiles and re-rank search results.A topic directory method is proposed to calculate the semantic similarity, which takes multi-inheritance into consideration, and then optimize the computing process based on the tree structure of inheritance relationship. Experiments are conducted to compare our method with the popular directory based search methods (e.g., Google Directory Search). Experimental results show that the proposed method in this paper can effectively capture personalization and improve the accuracy of personalized search over existing approaches.

Ontology-Based Informatin Retrieval Under a Mobile Business Environment

2011 ◽  
pp. 509-526
Author(s):  
Sheng-Uei Guan
Keyword(s):  
Information Retrieval ◽  
Query Optimization ◽  
Relevance Feedback ◽  
Fitness Function ◽  
Business Environment ◽  
Information Retrieval System ◽  
Optimization Techniques ◽  
Mobile Business ◽  
New Methodologies

The proposed OntoQuery system in the m-commerce agent framework investigates new methodologies for efficient query formation for product databases. It also forms new methodologies for effective information retrieval. The query formation approach implemented takes advantage of the tree pathway structure in ontology, as well as keywords, to form queries visually and efficiently. The proposed information retrieval system uses genetic algorithms, and is computationally more effective than iterative methods such as relevance feedback. Synonyms are used to mutate earlier queries. Mutation is used together with query optimization techniques like query restructuring by logical terms and numerical constraints replacement. The fitness function of the genetic algorithm is defined by three elements: (1) number of documents retrieved, (2) quality of documents, and (3) correlation of queries. The number and quality of documents retrieved give the basic strength of a mutated query, while query correlation accounts for mutated query ambiguities.

Sign in / Sign up

Export Citation Format

Share Document