CEPV: A Tree Structure Information Extraction and Visualization Tool for Big Knowledge Graph

A large amount of data with rich semantic and structural information has been accumulated in many real-world applications. In order to effectively describe the concepts and connections in these data sets, knowledge graph was proposed as a tool to handle it. The genealogy is a typical tree structure data and can be stored in the knowledge graph. However, due to the complexity and increasing volume of the data, how to efficiently extract and visualize the customized information from the big knowledge graph is hence a challenge and worthy of in-depth study. Motivated by this, we propose a novel user-specified information extraction and visualization tool, named CEPV (the Customized information Extracting, Processing and Visualization tool), for converting the big graph structure data into a specified tree structure display. The main steps of CEPV are as follows: firstly, according to the requirements of users, extracting the specified data from the massive, complex, heterogeneous data as fewer times as possible, which can reduce the frequency of database access and improve the overall efficiency of the algorithm. Secondly, the fault tolerance mechanism and attribute judgment rules are executed to ensure the correctness during the data processing. Finally, the processed data with a complex relationship is presented to the user in multiple visualization models. The high availability and effectiveness of our proposed tool is verified on a big knowledge graph dataset.