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Scalable visualization for DBpedia ontology analysis using Hadoop
Author(s) -
Park Seonghun,
Kim Sungmin,
Ha Youngguk
Publication year - 2015
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2310
Subject(s) - computer science , visualization , ontology , scalability , rendering (computer graphics) , big data , information retrieval , process (computing) , data visualization , world wide web , database , data mining , data science , artificial intelligence , programming language , philosophy , epistemology
Summary As ontologies are becoming larger and more diverse, ontological analysis and visualization of results have become more challenging, rendering the need for more computing resources. To address this issue, we suggest a system based on Hadoop for ontological analysis for large ontologies. Our suggested system consists of three parts: a data server to analyze ontological data, a visualization server to visualize the result of data analysis, and user applications to provide users with the visualized data. Server applications are implemented based on the Hadoop framework, and the ontological data are processed efficiently using the MapReduce algorithm. We performed ontological analysis using the DBpedia ontology and visualized the result. The goal of the visualization process is to determine the major properties of each class, and visualization is conducted on the Web in order to provide users with a cross‐platform environment. We evaluate the performance of the method by measuring execution times and analyzing experimental results obtained in the visualization process. The system we present is scalable for big ontological data. Copyright © 2015 John Wiley & Sons, Ltd.