Scalable visualization for DBpedia ontology analysis using Hadoop
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.
scalable visualization dbpedia ontology hadoop ontology visualization big data visualization dbpedia visualization