KGVis: an interactive visual query language for knowledge graphs

With the rise of artificial intelligence, knowledge graphs have been widely recognized as a cornerstone of AI. In recent years, more and more domains have been publishing knowledge graphs in different scales. However, it is difficult for end-users to query and understand those knowledge graphs consisting of hundreds of millions of nodes and edges. To improve the availability, accessibility, and usability of knowledge graphs, we have developed an interactive visual query language, called KGVis, which can guide end-users to gradually transform query patterns into query results. Furthermore, KGVis has realized the novel capability of flexible bidirectional transformations between query patterns and query results, which can significantly assist end-users to query large-scale knowledge graphs that they are not familiar with. In this paper, we present the syntax and semantics of KGVis, discuss our design rationale behind this interactive visual query language, and demonstrate various use cases of KGVis.