A Knowledge Base Visual Analytics Technique for Semantic Web

One of the technologies underpinning the future vision of the Web as huge database is Linked Data (LD). LD provides structured data over the Web that is understandable by machines. Therefore, it enables smart queries between different datasets over the Web. Consequently, effective Visual Analytics (VAs) techniques become necessary to efficiently extract and visualize the desired information from these data graphs. In this paper, we propose a theoretical VAs framework for LD as an approach for the automatic suggestion of information visualization graphs. The key objective of the framework is to amplify user perception by suggesting the best visual representation such as bar chart, map, and timeline of the selected data. The core of the framework is a well-defined artificial knowledge base information visualization ontology for automating the visualization process. The framework helps in the analysis process of data by providing the best visual representation to produce accurate decisions and to pave the road for next generation of VAs tools for the semantic web data.