Existing knowledge graph query approaches, whether traditional textual query languages or visual query languages, have steep learning curves that are unfriendly for non-expert users. This demonstration presents a Visual Query approach based on Full-Text search for knowledge graphs, called VQFT, which simplifies the process of querying knowledge graphs for users. Inspired by full-text search techniques, VQFT aims to combine the user-friendliness of visual query with the intuitiveness of full-text search , enabling users to query knowledge graphs as straightforward as using a search engine. Faceted full-text indexes, visual query constructor , and an interactive user interface are designed to achieve this goal. User tests and surveys have demonstrated that VQFT is more user-friendly and easier to learn than existing methods, which simplifies the construction of knowledge graph queries for non-expert users.

Visual query is a vital technique for comprehending and analyzing knowledge graphs, which provides an effective method to lower the barrier of querying knowledge graphs for non-professional users. Nevertheless, visual query techniques for knowledge graphs and ontologies that have emerged in recent years cannot bridge the gap between global information provided by the knowledge graph schema and underlying data of knowledge graph. Thus it cannot fully exploit the global information to navigate users for querying knowledge graphs. This demonstration showcases KGNav, a Knowledge Graph Navigational visual query system. KGNav (1) redefines the minimal unit of operation to abstract the conceptual hierarchy, i.e., Knowledge Graph Schema, in the domain from the original knowledge graph in an offline semi-automatic way through the equivalence relations between these units; it also (2) provides a series of operators and an interactive GUI to capture user query intentions, guiding users to explore the Knowledge Graph Schema to achieve in-depth analysis of knowledge graphs. We will demonstrate the capability of KGNav in reducing tedious queries, enabling users to swiftly grasp the structure of the knowledge graph, and performing queries through several fundamental scenarios.