GraphLingo: Domain Knowledge Exploration by Synchronizing Knowledge Graphs and Large Language Models

Knowledge graphs (KGs) are routinely curated to provide factual data for various domain-specific analyses. Nevertheless, it remains nontrivial to explore domain knowledge with standard query languages. We demonstrate GraphLingo, a natural language (NL)-based knowledge exploration system designed for exploring domain-specific knowledge graphs. It differs from conventional knowledge graph search tools in that it enables an interactive exploratory NL query over domain-specific knowledge graphs. GraphLingo seamlessly integrates graph query processing and large language models with a graph pattern-based prompt generation approach to guide users in exploring relevant factual knowledge. It streamlines NL-based question & answer, graph query optimization & refining, and automatic prompt generation. A unique feature of GraphLingo is its capability to enable users to explore by seamlessly switching between a more ‘open’ approach and a more relevant yet ‘conservative’ one, facilitated by diversified query suggestions. We show cases of GraphLingo in curriculum suggestion, and materials scientific data search.