Bat Algorithm for Efficient Query Expansion: Application to MEDLINE

Khennak, Ilyes; Drias, Habiba

doi:10.1007/978-3-319-31232-3_11

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…Bat algorithm for efficient query expansion: Application to MEDLINE [6] It is a combinatorial optimization The bat algorithm aims to extract the best expansion keywords, in one hand and to select the best relevant documents to the expanded query on other hand…”

Section: Comparison Resultsmentioning

confidence: 99%

“…Bat algorithm for efficient query expansion: Application to MEDLINE [6] Bat-Inspired Approach is used to improve the computational cost. Then, this approach is used to retrieve both the best expansion keywords and the best relevant documents simultaneously.…”

Section: Comparison Resultsmentioning

confidence: 99%

“…They have used Page Rank algorithm to implement this technique. They have implemented it in three steps as follows: (i) 1) Calculation of user interest 2) Growth analysis Rate 3) Total site linking D. Bat algorithm (BA) [6] In this paper [6], they have used swarm intelligence to solve the problem of query expansion, First, they have calculated rank of the pseudo relevant documents using document scoring function like OKAPI 25, next they have extracted keyword candidate using term scoring function e.g. RSJ, Rocchio, next they have put the best ranked keyword in original query, and finally the document is retrieved by using document scoring function.…”

Section: Literature Surveymentioning

confidence: 99%

“…The problems are solved in nature like foraging for food, prey evading, and colony relocation through SI. The information is stored and transferred by the means of agents such as proximity in fish and birds, pheromones in ants and dancing in bees [1] [6]. …”

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Comparing Performance of Various Optimization Algorithms for Effective Information Retrieval – A Review

Shaikh¹

2017

IJRASET

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Keywords: Information Retrieval; optimization; Accelerated particle swarm intelligence algorithm; Bat algorithm; Query Expansion I. OBJECTIVE The objective of this paper is to compare various optimization algorithms in information retrieval domain. The researchers have used various optimization algorithms to solve the problems for information retrieval. Here we have studied various information retrieval optimization algorithms. Different algorithms have been implemented on different sets of databases like MEDLINE database, Web database etc. We are comparing these algorithms based on effectiveness. II. INTRODUCTION A. Information RetrievalDuring the last decade the information over the web have increased and optimization of information retrieval effectiveness has driven the quality of the results over the web, People are more trusting and preferring web search as a source of information. Information retrieval has come out of academic discipline to become the basis of most preferred and reliable source of information. The field of information retrieval began with scientific library records and scientific publications; it spread rapidly in other domains like journalism, lawyers and medical fields. Information retrieval then spread in web information access. The information retrieval provides solution in finding relevant information in unstructured information [5]. B. Optimization (Swarm Intelligence)The researchers have used number of optimization technique in information retrieval domain. There are various models of IR and methods for optimization. Here we are concentrating on one of stochastic optimization technique called swarm intelligence. Swarm intelligence is the study of computational systems inspired by the 'collective intelligence'. Collective Intelligence emerges through the working together of large numbers of similar agents in the environment. Schools of fish, flocks of birds, and colonies of ants are some the examples. The property of swarm intelligence is self-organization, decentralization and distribution throughout the environment. The problems are solved in nature like foraging for food, prey evading, and colony relocation through SI. The information is stored and transferred by the means of agents such as proximity in fish and birds, pheromones in ants and dancing in bees [1] [6].

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Section: Comparison Resultsmentioning

confidence: 99%

Section: Comparison Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

mentioning

confidence: 99%

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Comparing Performance of Various Optimization Algorithms for Effective Information Retrieval – A Review

Shaikh¹

2017

IJRASET

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“…From this term pool, genetic algorithm was used to select a combination of terms to add to the initial query. Khennak and Drias [20] designed a bat algorithm based query expansion for Web medical information retrieval. The authors used bat algorithm to retrieve simultaneously the appropriate expansion keywords and the best relevant documents to the expanded query.…”

Section: Related Work On Qementioning

confidence: 99%

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Khennak

Drias

2017

Appl Intell

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Swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle Swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web's largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

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Particle Swarm Optimization for Query Items Re-rating

Khennak

Drias

2021

Advances in Intelligent Systems and Computing

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Bat Algorithm for Efficient Query Expansion: Application to MEDLINE

Cited by 9 publications

References 9 publications

Comparing Performance of Various Optimization Algorithms for Effective Information Retrieval – A Review

Comparing Performance of Various Optimization Algorithms for Effective Information Retrieval – A Review

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Particle Swarm Optimization for Query Items Re-rating

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