Web-Based Evolutionary and Adaptive Information Retrieval

Kushchu, Ibrahim

doi:10.1109/tevc.2004.842093

Cited by 26 publications

(15 citation statements)

References 33 publications

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“…A related research area deals with the development of evolving agents that crawl the Web to search for topical material [26]. A comprehensive literature review of Web-based evolutionary algorithms can be found in [22].…”

Section: Discussionmentioning

confidence: 99%

Evolving Disjunctive and Conjunctive Topical Queries based on Multi-objective Optimization Criteria

Cecchini¹,

Lorenzetti²,

Maguitman³

2010

Int. Artif.

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In this work we propose techniques based on single-and multi-objective evolutionary algorithms to automatically evolve a population of topical queries. The developed techniques can be applied in the implementation of a topical search system. We report on the results of different strategies that attempt to evolve conjunctive and disjunctive queries. Our analysis reveals the limitations of the single-objective approach and highlights the advantages of applying multi-objective evolutionary algorithms for the problem at hand. In addition, we observe that disjunctive queries have the potential to achieve better retrieval performance than conjunctive queries. Finally, we show that the multi-objective evolutionary approach results in better performance than a baseline and other state-of-the-art techniques for query refinement.

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Section: Discussionmentioning

confidence: 99%

Evolving Disjunctive and Conjunctive Topical Queries based on Multi-objective Optimization Criteria

Cecchini¹,

Lorenzetti²,

Maguitman³

2010

Int. Artif.

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“…Other approaches based on different models were proposed, some in particular were built upon the ant colony paradigm [10] [11]. Kushchu [12] proposed a survey of evolutionary and adaptive approaches applied to Information Retrieval on the Web.…”

Section: A Focused Crawling and Artificial Lifementioning

confidence: 99%

Population Control for Multi-agent Based Topical Crawlers

Mouton

Marteau²

2008

2008 Eighth International Conference on Intelligent Systems Design and Applications

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Abstract-The use of multi-agents topical crawlers based on the model of endogenous fitness introduces the problem of population control. We propose to harness this point through an energy based model to balance the reproduction/life expectency of agents. Our goal is to ease the tuning of parameters and to optimize the use of available ressources for the crawling. We introduce a model based on energy designed to control the ratio number of agents over precision of the crawling. We present some experiments that show that the size of the population remains under control during the crawling.

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“…Web information retrieval system [1][2][3] desperately need good document clustering algorithm to categorize documents automatically to retrieve information more easily. The advancement of web usage pose a new challenge for the researchers to develop effective document clustering algorithm to obtain effective results [4,5,10,11] with less computational task.…”

Section: Introductionmentioning

confidence: 99%

Document-Document similarity matrix and Multiple-Kernel Fuzzy C-Means Algorithm-based web document clustering for information retrieval

Yadav¹

2014

International Journal of Advanced Research in Computer and Comm

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Due to continuous development of World Wide Web, web database are growing massively where automatic grouping of web documents pose a new challenge for researchers to easily retrieve the information. Literature presents different algorithms for web document clustering useful for information retrieval. In this work, Document-Document similarity matrix and Multiple-Kernel Fuzzy C-Means Algorithm-based web document clustering is developed for information retrieval. At first, web documents are read and initial pre-processing are applied to extract the important words. Then, feature space is constructed using keywords and its frequency. Subsequently, document to document similarity matrix is constructed using the similarity measure, called semantic retrieval measure (SR). The measure considers four different criteria, such as, the probability of occurrence in the document, probability of occurrence in the first document, probability of occurrence in the second document and probability of occurrence in both synonyms set. Based on this measure, D-D matrix is computed to do the final grouping using Multiple-Kernel Fuzzy C-Means Algorithm. The experimentation is done with 100 web documents and the results are evaluated with accuracy and entropy.

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Web-Based Evolutionary and Adaptive Information Retrieval

Cited by 26 publications

References 33 publications

Evolving Disjunctive and Conjunctive Topical Queries based on Multi-objective Optimization Criteria

Evolving Disjunctive and Conjunctive Topical Queries based on Multi-objective Optimization Criteria

Population Control for Multi-agent Based Topical Crawlers

Document-Document similarity matrix and Multiple-Kernel Fuzzy C-Means Algorithm-based web document clustering for information retrieval

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