VISION KG: Topic centric Visualization System for Summarizing Knowledge Graph
Large scale knowledge graph (KG) has attracted wide attentions in both academia and industry recently. However, due to the complexity of SPARQL syntax and massive volume of real KG, it remains difficult for ordinary users to access KG. In this demo, we present VISION KG, a topic centric visualization system to help users navigate KG easily via entity summarization and entity clustering. Given a query entity v0, VISION KG summarizes the induced subgraph of v0's neighbor nodes via our proposed facts ranking method that measures importance, relatedness and diversity. Moreover, to achieve conciseness, we split the summarized graph into several topic centric summarized subgraph according to semantic and structural similarities among entities. We will demonstrate how VISION KG provides a user friendly visualization interface for navigating KG.
WSDM