Knowledge Representation Models and Cognitive Search Support Tools

Maksimov, N. V.; Golitsina, O. L.; Monankov, K. V.; Gavrilkina, Anastasia

doi:10.1016/j.procs.2020.02.118

Cited by 4 publications

(2 citation statements)

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“…In [2] author focuses on the concept of cognitive search, which involves the formation of an ontology of a subject area and the interaction of information retrieval systems with human cognition. The paper proposes a model where information retrieval and analysis mirror the operations performed by human consciousness in extracting and synthesizing knowledge.…”

Section: [2] Literature Surveymentioning

confidence: 99%

"Enhancing Information Retrieval in Azure Cognitive Search"

Parwani,

Das,

Raj

et al. 2023

JAC

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This paper explores the use of cognitive search in improving information retrieval for national paper publication. By leveraging artificial intelligence and natural language processing techniques, cognitive search systems enhance keyword-based search engines by understanding user queries holistically, extracting relevant concepts, and providing personalized recommendations. We discuss the specific requirements and challenges faced by researchers in the publication process, highlight successful case studies, and emphasize the need for continued innovation in this field.

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Section: [2] Literature Surveymentioning

confidence: 99%

"Enhancing Information Retrieval in Azure Cognitive Search"

Parwani,

Das,

Raj

et al. 2023

JAC

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“…The search technologies are used for performing operations like determination of relevance, usefulness, significance, and combination [ 4 ]. The ontology is the main general knowledge representation design that is utilized for information retrieval and it indicates the knowledge by the terms of information hierarchies of the processable, understandable, and machine-readable.…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval

Kumar

Sharma

2022

J Supercomput

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Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques.

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