A Fuzzy Rule-Based Agent for Web Retrieval-Filtering

Vrettos, S.; Stafylopatis, Andreas

doi:10.1007/3-540-45490-x_58

Cited by 16 publications

(9 citation statements)

References 5 publications

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“…The techniques that are applied to this end are as diverse as fuzzy clustering [47], fuzzy association rules [58] and again fuzzy if-then rules [108]. This further illustrates the fact that fuzzy set theory can often provide a flexible vehicle for implementing advanced systems.…”

Section: Manipulation Of Search Resultsmentioning

confidence: 99%

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Fuzzy Methods on the Web: A Critical Discussion

Schockaert

Makarytska

Cock

2010

35 Years of Fuzzy Set Theory

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Gradual concepts abound in many web-related domains, ranging from the notion of relevance in information retrieval, to the strength of connection in online social networks. As a result, fuzzy set theory is often a natural choice in implementing web systems. In this chapter, we give an overview of applications of fuzzy set theory in this area, focusing in particular on information retrieval, the semantic web, and recommender systems. In each case, we contrast fuzzy methods with other approaches, analyzing why and how the ideas of fuzzy set theory may be beneficial.

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Section: Manipulation Of Search Resultsmentioning

confidence: 99%

“…Other applications where fuzzy methods have been proposed to manipulate search results are: relevance feedback [21,59,108], meta-search [60], and query expansion [47,58]. The techniques that are applied to this end are as diverse as fuzzy clustering [47], fuzzy association rules [58] and again fuzzy if-then rules [108].…”

Section: Manipulation Of Search Resultsmentioning

confidence: 99%

Fuzzy Methods on the Web: A Critical Discussion

Schockaert

Makarytska

Cock

2010

35 Years of Fuzzy Set Theory

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“…Precision and recall are two standard quantitative measures of the performance achieved by information retrieval models [74]. Precision indicates the capacity of a system to retrieve only the relevant information items, whereas recall indicates the capacity of a system to retrieve all the relevant information items.…”

Section: Performance Measuresmentioning

confidence: 99%

A knowledge-based model using ontologies for personalized web information gathering

Tao

Zhong

2010

Web Intelligence and Agent Systems: An International Journal

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Nowadays, how to gather useful and meaningful information from the Web has become challenging to all users because of the explosion in the amount of Web information. However, the mainstream of Web information gathering techniques has many drawbacks, as they are mostly keyword-based. It is argued that the performance of Web information gathering systems can be significantly improved if user background knowledge is discovered and a knowledge-based methodology is used. In this paper, a knowledge-based model is proposed for Web information gathering. The model uses a world knowledge base and user local instance repositories for user profile acquisition and the capture of user information needs. The knowledge-based model was successfully evaluated by comparing a manually implemented user concept model. The proposed knowledge-based model contributes to better designs of knowledge-based and personalized Web information gathering systems.

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“…The personal computer as the nerve centre of an e‐learning system is depicted in Figure 2. Currently, a variety of e‐learning systems are in existence, ranging from proprietary solutions to systems that use technologies that originally had not been developed for teaching and learning, eg, the World Wide Web (WWW) (Chung Leung & Li, 2001; Cloete, 2001; Cortes et al , 1999; Vrettos & Stafylopatis, 2001). These systems generally employ artificial intelligence for data retrieval/management and ICT for service utilisation.…”

Section: A Typical E‐learning Systemmentioning

confidence: 99%

An autonomous mobile agent‐based distributed learning architecture: a proposal and analytical analysis

Ahmed

Sadeq

2006

Brit J Educational Tech

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Current distance learning systems are increasingly packing highly dataintensive contents on servers, resulting in the congestion of network and server resources at peak service times. A distributed learning system based on faded information field (FIF) architecture that employs mobile agents (MAs) has been proposed and simulated in this work. The learning contents are decentralised in the proposed system, and these are dynamically distributed around the main education site server on different nodes in response to network congestion and server overload. The system simulation results have been presented and analysed. The proposed system holds the potential to address the network congestion and server-overload problems in distance learning systems.

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A Fuzzy Rule-Based Agent for Web Retrieval-Filtering

Cited by 16 publications

References 5 publications

Fuzzy Methods on the Web: A Critical Discussion

Fuzzy Methods on the Web: A Critical Discussion

A knowledge-based model using ontologies for personalized web information gathering

An autonomous mobile agent‐based distributed learning architecture: a proposal and analytical analysis

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