• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2021, Volume: 14, Issue: 21, Pages: 1775-1785

Original Article

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Received Date:24 April 2021, Accepted Date:21 May 2021, Published Date:19 June 2021


Objectives: To refine search performance using semantic web with an improved algorithm to retrieve the information efficiently. Methods: In order to establish the SCBR model and improve the performance of Web search, this paper adopts the Natural Language Processing (NLP) technology and the Quality of Service (QoS) ranking method, and endeavors to develop a relevant reliable and efficient search engine. Findings: Mean average precision tests revealed for quickness and precious of search results, and achieves the values from 82.98% to 99.53%. The experimental results show that the NLP technique improves the performance of SCBR model, and achieves higher average precision and recall values. Novelty: This research focuses to develop a related reliable and an efficient search engine to retrieve the accurate results for the user’s complex query. It even bears the human error in typing, and suggests the expected word to search for. It also aims to retrieving the same result for synonym words which prevent the appearance of irrelevant search results.


­ Semantic Web, Information Retrieval, SCBR Model, Ontology, Semantic Search, Quality of Service


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© 2021 Palaniammal. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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