Using genetic algorithms to evolve a population of topical queries

Cecchini, Rocío L.; Lorenzetti, Carlos M.; Maguitman, Ana Gabriela; Brignole, Nélida Beatríz

doi:10.1016/j.ipm.2007.12.012

Cited by 29 publications

(21 citation statements)

References 15 publications

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“…In [9] we proposed to apply single-objective genetic algorithms to evolve AND-queries. In that case we used the Web as a corpus for training the algorithm and the optimization criteria were based on the similarity of the retrieved material to the topic of interest.…”

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.

Self Cite

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“…The candidate terms of term pool or top retrieved feedback document are ranked based on the BIM values obtained from Equation (14). Candidate terms ranked based on the BIM score, after ranking some high BIM scored candidate terms are selected for expanding the user query.…”

Section: And O(r | T) Is the Probability Odds Of Relevance Of A Term:mentioning

confidence: 99%

“…Araujo et al [13] have used a Genetic algorithm for query expansion based on stemming and morphological variations. The authors in [14] present a new method for query reweighting to deal with document retrieval. The proposed method uses Genetic Algorithms to reweight a user's query vector, based on the user's relevance feedback, to improve the performance of document retrieval systems.…”

Section: Introductionmentioning

confidence: 99%

Ranks Aggregation and Semantic Genetic Approach based Hybrid Model for Query Expansion

Singh¹

2017

IJCIS

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Effective query expansion terms selection methods are really very important for improving the accuracy and efficiency of Pseudo-Relevance Feedback (PRF) based automatic query expansion techniques in information retrieval system. These methods remove irrelevant and redundant terms from the top retrieved feedback documents with respect to a user query. Individual terms selection methods have been widely investigated for improving its performance. However, it is always a challenging task to find an individual expansion terms selection method that would outperform other individual methods in most cases. In this paper, first we explore the possibility of improving the overall performance using individual terms selection methods. Second, we propose a model for combining multiple expansion terms selection methods by using a variety of ranks combining approaches. Third, semantic filtering used to filter out semantically irrelevant terms obtained after combining multiple terms selection methods. Fourth, the Genetic Algorithm used to make an optimal combination of query terms and candidate expansion terms obtained by applying ranks combination and semantic filtering approach. Our experimental results demonstrated that our proposed approaches achieved a significant improvement over each individual terms selection methods and related state-of-the-arts approaches.

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“…In the context of IR optimization, stochastic approaches have been exploited to improve the IR effectiveness; for example genetic algorithms have been used for improving the effectiveness of IR systems [36,37,41], for query reformulation [38], for query selection [17] and improving [57]. Other techniques make use of Fuzzy algorithms [33,47], local context analysis [56], clustering [59], and ranking improvement [54].…”

Section: Introductionmentioning

confidence: 99%

Optimization of information retrieval for cross media contents in a best practice network

Bellini

Cenni

Nesi

2014

Int J Multimed Info Retr

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Recent challenges in information retrieval are related to cross media information in social networks including rich media and web based content. In those cases, the cross media content includes classical file and their metadata plus web pages, events, blog, discussion forums, comments in multilingual. This heterogeneity creates large complex problems in cross media indexing and retrieval for services that integrate qualified documents and user generated content together. Problems are also related to scalability, robustness and resilience to errors. Moreover, users expect to have fast and efficient indexing and searching services, from social media in best practice network services. This paper presents a model and an indexing and searching solution for cross media contents, addressing the above issues, developed for the ECLAP Social Network, in the domain of Performing Arts. Effectiveness and optimization analysis of the retrieval solution are presented with relevant metrics. The research aimed to cope with the complexity of a heterogeneous indexing semantic model, using stochastic optimization techniques, with tuning and discrimination of relevant metadata terms. The research was conducted in the context of the ECLAP European Commission project and services

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Using genetic algorithms to evolve a population of topical queries

Cited by 29 publications

References 15 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

Ranks Aggregation and Semantic Genetic Approach based Hybrid Model for Query Expansion

Optimization of information retrieval for cross media contents in a best practice network

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