An Improved Semantic Items Generation Algorithm for Mining High Number of Significant Association Rules from Semantic Web Data

Abstract: The number of Ontologies and the Linked Open Data (LOD) on the Web is constantly increasing. This type of complex and heterogeneous semi-structured data raises new opportunities and challenges for the data mining (DM) community. Semantic Web Association Rule Mining (SWARM) algorithm have the limitations of generating many non-interesting, duplicate and equivalents rules and low algorithm performance. This research demonstrates a procedure for improving the performance of SWARM in text mining by using RDF data. The result revealed significant reduction in the number of generated rules this is significant because it helps to address the problem of discovering duplicate/equivalent Association Rules from Semantic Web Data.

Keywords: Semantic Web, Association Rules, Data Mining, Linked Data.

Title: An Improved Semantic Items Generation Algorithm for Mining High Number of Significant Association Rules from Semantic Web Data

Author: Isya Isyaku, Muhammad Sirajo Aliyu, Abubakar Aminu Muazu

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 11, Issue 2, April 2023 - June 2023

Page No: 77-86

Research Publish Journals

Website: www.researchpublish.com

Published Date: 15-May-2023

DOI: https://doi.org/ 10.5281/zenodo.7936390

Paper Download Link (Source)

https://www.researchpublish.com/papers/an-improved-semantic-items-generation-algorithm-for-mining-high-number-of-significant-association-rules-from-semantic-web-data