Bayesian rough set based information retrieval

Sharma, Añil; Kumar, Suresh

doi:10.1080/09720510.2020.1799575

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…But it is not suitable if we are having degree of relevance in term weights among the query term. To resolve rough comparison between query and web documents for IR where exact match is not found, RSA, equivalence classes, Rough relations (equality, inclusion and overlap), relevance feedback techniques have been proposed in [27], which is non-pattern matching intelligent IR method. But proposed intelligent IR method intelligent IR intolerance to misclassification of index terms.…”

Section: Discussion and Analysismentioning

confidence: 99%

“…The classical RS is successfully applied in IR by many researchers [26,27,28,29,30,31,32,33]. These proposals were based on rough set approximations, rough relations and indiscernibility (equivalence) relation.…”

Section: Rough Set Theory In Information Retrievalmentioning

confidence: 99%

“…These proposals were based on rough set approximations, rough relations and indiscernibility (equivalence) relation. Additionally, user's relevance feedback was employed to enhance the effectiveness of these IR models [26,27,29]. Some authors also introduced formal concept analysis (FCA) and ontology with RS approximations in IR proposals.…”

Section: Rough Set Theory In Information Retrievalmentioning

confidence: 99%

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Rough set generalization models based information retrieval

Sharma

Kumar

Lathar³

et al. 2022

ijhs

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World Wide Web has become the largest pool of knowledge in terms of volume, but web users still continue to have difficulty in finding the relevant information. Two issues influence the output of information retrieval (IR) systems: first, information overload and second, the vagueness and imprecision in document representations as well as user information need. Rough set theory (RST) proved successful in tackling ambiguous, uncertain, imprecise, or incomplete information. RST is being used in a range of artificial intelligence applications as a result of this. RST has several uses, one of which is IR. This survey focused on Rough Sets and their generalization models in IR. Some previous studies attempted to comprehend rough set-based IR models, but their scope was limited. This research examines various rough set-based IR models, their basic methodologies, features, strengths, and limitations systematically. For critical analysis, a comparison of the existing frameworks is also presented.

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Section: Discussion and Analysismentioning

confidence: 99%

Section: Rough Set Theory In Information Retrievalmentioning

confidence: 99%

Section: Rough Set Theory In Information Retrievalmentioning

confidence: 99%

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Rough set generalization models based information retrieval

Sharma

Kumar

Lathar³

et al. 2022

ijhs

Self Cite

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“…It has made an impressive success story in the digital world in terms of its simplicity and easy access to information users. It only became possible by using various Information Retrieval (IR) and Computational Intelligence (CI) techniques [1]. These techniques have been playing a vital role in fetching the desired information and making it available to users to access old as well as new content on the Internet.…”

Section: Introductionmentioning

confidence: 99%

Ontology-based soft computing and machine learning model for efficient retrieval

Anand,

Kumar

2023

Knowl Inf Syst

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Unstructured and unorganized data always degrade the performance of search techniques and produce irrelevant results in response to the query as well as decrease the speed of retrieval results. Ontology in Semantic Web (SW) provides an adequate solution to represent the knowledge, because of its backbone knowledge of an application or domain. But, a domain ontology has three basic problems while retrieving useful knowledge from a domain ontology-structuring/arrangement, unnecessary knowledge reduction, selection and extraction, and speeding up the retrieval process. To handle such a problem, the MLK-rBO model is proposed that works for four different phases-clustering, knowledge discovery, building a probabilistic network, and model evaluation using the ensemble approach of three different techniques namely clustering, rough set, and Bayesian network (BN). Finally, the proposed model is tested with statistical parameters and compared with other models namely DT, RF, and SVM to check the effectiveness of MLK-rBO. By analyzing the experimental results, we observed that the MLK-rBO gives better accuracy (98.36 %) than the available models.

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Enhancing Abstractive Text Summarisation Using Seq2Seq Models: A Context-Aware Approach

Kumar,

Kumar

2024

2024 International Conference on Automation and Computation (AUTOCOM)

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Bayesian rough set based information retrieval

Cited by 14 publications

References 30 publications

Rough set generalization models based information retrieval

Rough set generalization models based information retrieval

Ontology-based soft computing and machine learning model for efficient retrieval

Enhancing Abstractive Text Summarisation Using Seq2Seq Models: A Context-Aware Approach

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