Fuzzy Information Retrieval Model Research Articles

Intelligent fuzzy information retrieval based on ontology knowledge-base

Intelligent fuzzy information retrieval (IR) based on ontology knowledge base has become one of the most active research directions in the field of intelligent IR system. How to use ontology knowledge base to further improve its retrieval performance and intelligence has become the main research goal of intelligent fuzzy IR system based on ontology knowledge-base. IR using knowledge organisation can improve the quality between returning document and user's initial query. The intelligent fuzzy information retrieval model proposed in this paper can provide a coded knowledge-base structure for IR, which is composed of multiple related ontologies, and the relationship between ontology is expressed as fuzzy relationship. In this kind of knowledge organisation, a new method is used to extend user's initial query and index document set, which independently represents ontology and the relationship between concepts. The experimental results show that the proposed model has a better overall performance compared with the other classical IR method. The prospects for future development and suggestions for possible extensions of the ontology knowledge base-based intelligent fuzzy IR systems are also discussed.

Intelligent fuzzy information retrieval based on ontology knowledge-base

Intelligent fuzzy information retrieval (IR) based on ontology knowledge base has become one of the most active research directions in the field of intelligent IR system. How to use ontology knowledge base to further improve its retrieval performance and intelligence has become the main research goal of intelligent fuzzy IR system based on ontology knowledge-base. IR using knowledge organisation can improve the quality between returning document and user's initial query. The intelligent fuzzy information retrieval model proposed in this paper can provide a coded knowledge-base structure for IR, which is composed of multiple related ontologies, and the relationship between ontology is expressed as fuzzy relationship. In this kind of knowledge organisation, a new method is used to extend user's initial query and index document set, which independently represents ontology and the relationship between concepts. The experimental results show that the proposed model has a better overall performance compared with the other classical IR method. The prospects for future development and suggestions for possible extensions of the ontology knowledge base-based intelligent fuzzy IR systems are also discussed.

An Enhanced Multi-view Fuzzy Information Retrieval Model based on Linguistics

The paper proposes a linguistic based multi-view fuzzy ontology information retrieval model. It deals with multi-view linguistic based queries in multi domains. Such linguistics are user defined, reflecting his subjective view. The model also proposes a ranking algorithm that ranks the set of relevant documents according to some criteria such as their relevance degree, confidence degree, and updating degree.

An adaptive fuzzy information retrieval model to improve response time perceived by e-commerce clients

In this paper, an adaptive fuzzy logic-based information retrieval model is presented to enable users retrieve exact and specific information they sort after. The proposed IR model takes into consideration the limited bandwidth between ISP and its users; and the characteristics (processor speed, memory size, resolution, availability of anti-virus, etc.) of clients’ devices in ensuring that a customer has a fruitful and eventful session while conducting business online. The model was designed using unified modelling language and implemented using Borland JBuilder. A performance evaluation of the proposed information retrieval system using two evaluation measures was conducted. The experimental result indicated that the model has an acceptable performance.