Astoundingly Smart System Furnishing Ranking of Big Data in Search Engines

Kakoli Banerjee; Shishir Dua

doi:10.57159/gadl.jcmm.1.1.22018

Authors

Kakoli Banerjee Department of Computer Science and Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India 201301
Shishir Dua Department of Computer Science and Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India 201301

DOI:

https://doi.org/10.57159/gadl.jcmm.1.1.22018

Keywords:

YAGO dataset, DBpedia, Ontology, Semantic Web, NoSQL

Abstract

The abruptly escalating internet is sensational. It inculcates a humungous volume of big data, which is obsolete and tedious to manage, scrutinize, analyze and perform operations upon them in conventional ways. Big data has thus expedited the search and retrieval of information, necessitating the development of contemporary search algorithms to aid this process. However, the primary hindrance for the first and second generations of conventional search engines was the syntax of keywords devoid of semantic meaning and the lack of a knowledge base that linked disparate web material. This article presents a framework based on trendy technologies, specifically Extracting, Transforming and Integrating (ETI) processes, ontology graphs, and indexing Resource Description Framework (RDF) using the wide-column Not only Structured Query Language (NoSQL) method. The most significant contribution in this regard is developing a mathematical model to compute the similarity score between a query and stored RDF documents using semantic relations. Numerous operations were carried out to evaluate the effectiveness of the proposed methodology in installing data sources, such as DBpedia and YAGO dataset. Insofar as experimental results are concerned, the suggested model achieves greater precision than other comparable systems.

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