Context-Based Query Expansion Method for Short Queries Using Latent Semantic Analyses

Ghali, Btihal El; Qadi, Abderrahim El; Ouadou, Mohamed; Aboutajdine, Driss

doi:10.1007/978-3-319-26850-7_33

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…Contrary to traditional query expansion techniques that is based on the computation of two-dimensional cooccurrence matrices, the authors proposed a new QE technique with three-dimensional matrices. Still in the same direction, El Ghali et al [11] presented a context-based query expansion method for Web short queries. The authors selected the best expansion keywords using Latent Semantic Analyses (LSA) method which based on the result of the three following query suggestion methods: the Cosine Similarity (CS), the Language Models (LM), and their fusion.…”

Section: Related Work On Qementioning

confidence: 98%

“…Equation 11 consists of three terms. The first term determines the current position of the ith particle.…”

Section: The Accelerated Psomentioning

confidence: 99%

“…The best solution x * is then initialized by the best solutions of all the particles. The APSO iteratively moves from the initial set of solutions to the best solution by updating the position x i of each particle i using (11). Solutions are continuously adjusted based on the continuous flying iterations.…”

Section: The Apso Algorithmmentioning

confidence: 99%

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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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Section: Related Work On Qementioning

confidence: 98%

“…Equation 11 consists of three terms. The first term determines the current position of the ith particle.…”

Section: The Accelerated Psomentioning

confidence: 99%

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

Khennak

Drias

2017

Appl Intell

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“…6, pp: 2053-2063 2055 the query. Three methods of query suggestion were used to extract the context from the search engine, namely the cosine similarity, the language models, and their fusion [12]. In 2018, Jabri et al suggested a similarity measure using the query graph.…”

Section: Related Workmentioning

confidence: 99%

Applying Similarity Measures to Improve Query Expansion

Hussain

2021

eijs

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The huge evolving in the information technologies, especially in the few last decades, has produced an increase in the volume of data on the World Wide Web, which is still growing significantly. Retrieving the relevant information on the Internet or any data source with a query created by a few words has become a big challenge. To override this, query expansion (QE) has an important function in improving the information retrieval (IR), where the original query of user is recreated to a new query by appending new related terms with the same importance. One of the problems of query expansion is the choosing of suitable terms. This problem leads to another challenge of how to retrieve the important documents with high precision, high recall, and high F measure. In this paper, we solve this problem through applying different similarity measures with the use of English WordNet. The obtained results proved that, with a suitable selection method, we are able to take advantage of English WordNet to improve the retrieval efficiency. The work proposed in this paper is extracting the terms from all the documents and query, then applying the following steps: preprocessing, expanding the query based on English WordNet, selecting the best terms, weighting of term, and finally using the cosine similarity and Jaccard similarity to obtain the relevant documents. Our practical results were applied on the DUC2002 dataset that contains 559 documents distributed over several categories. The average precision of cosine (for random queries) = 100% whereas the average precision of Jaccard = 84.4 %, and the average recall of cosine = 86.8% whereas the average recall of Jaccard = 73.4%. The average f-measure of cosine = 92%, whereas the average f-measure of Jaccard = 76%.

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“…The second one is the involvement of machine learning techniques in order to select and rank the entities oriented for query expansion. El Ghali et al [11] proposed a query expansion method for Web short queries using the Latent Semantic Analyses (LSA) technique which is based on the context around the query. This context is extracted from the search engine query logs by a three-query suggestion method: The Cosine Similarity, the Language Models, and their fusion.…”

Section: Related Workmentioning

confidence: 99%

Improving Retrieval Performance Based on Query Expansion with Wikipedia and Text Mining Technique

Jabri¹,

Dahbi²,

Gadi³

et al. 2018

IJIES

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Abstract:Textual query is the simple mean for communicating with a retrieval system. However, there is a risk of providing an incomplete query which hinders the system from satisfying the user information needs. By reformulating the queries, query expansion is solution for this problem, this mainly relies on an accurate choice of the added terms to an initial query. It can yield a large number of irrelevant terms, which in turn negatively influences quality of retained documents. In this paper, we propose Query Expansion approach. It consists of reformulating queries by semantically related terms extracted from a semantic graph called query graph derived from Wikipedia. Furthermore, we propose a similarity measure which computes the similarity between a candidate terms and initial query using the query graph, Explicit Semantic Analysis (ESA) measure, and text mining technique. The experiments on Text Retrieval Conference (TREC) collection show that the proposed approach performs significantly better than the baseline system and some existing techniques.

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Context-Based Query Expansion Method for Short Queries Using Latent Semantic Analyses

Cited by 4 publications

References 6 publications

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

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

Applying Similarity Measures to Improve Query Expansion

Improving Retrieval Performance Based on Query Expansion with Wikipedia and Text Mining Technique

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