A classification using RDFLIB and SPARQL on RDF dataset. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Gupta, Rupal; Malik, Sanjay Kumar
• Is Part Of: Journal of information & optimization sciences. Volume 43:Issue 1(2022)
• Page Start: 143
• Page End: 154
• Publication Date: 2022-01-02
• Subjects: 68T99 -- 68P20; Machine learning (ML) -- Classification -- RDF -- RDFLib -- SPARQL -- Decision tree classifier -- Random forest -- Confusion matrix; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2022.2039461
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: Web Semantics is one of the prominent domain in the current era of web. This web comprises of linked open data, which is an interconnection of related data over web and the base of data representation is Resource Description Framework (RDF). Applying machine learning algorithms will enhance the capabilities of semantic web over the concepts to visualize and analyze data over web. It will integrate the capabilities of web semantics and machine learning. In this paper, classification on a sample RDF dataset has been implemented and their results have been analyzed. This will give researcher a baseline to work with machine learning algorithms on semantic web data. The machine learning algorithms like decision tree classifier, random forest classifier along with artificial neural network classification has been analyzed in this paper along with their accuracy measures. The integration of machine learning and semantic web, based on RDF based knowledge graphs is the singularity of the proposed work and represented using experimental results.