Recent advances in Large Language Models (LLMs) have drawn interest in their capacity for logical reasoning, an area traditionally dominated by symbolic systems that rely on complete, manually curated knowledge bases represented in formal languages. This paper introduces a framework that leverages pretrained LLMs to generate Description Logic (DL) class expressions from instance-level examples and background knowledge, translated to natural language. The baseline is Concept Induction, a symbolic learning approach that is mostly based on formal logical reasoning over a DL theory. Drawing inspiration from the DL-Learner architecture, our approach replaces traditional symbolic methods with LLM-based models to generate DL class expressions from instance-level data. We evaluate our approach using three benchmark ontologies across two LLMs: gpt-4o and o3-mini. We use a symbolic reasoner, Pellet, to verify the LLM-generated results and incorporate the reasoner’s feedback into our pipeline to ensure logical consistency, thereby generating a hybrid neurosymbolic system. By introducing controlled variations to the background knowledge, we assess the models’ reliance on commonsense versus formal reasoning. Results show that o3-mini achieves near-perfect accuracy across settings, albeit with longer runtime. These findings demonstrate that LLMs have the potential to serve as scalable and flexible DL learners when coupled in a hybrid neurosymbolic setting, offering a promising alternative to symbolic approaches—particularly in contexts where high-quality ontologies are incomplete or unavailable.

KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, various place and region identifiers, among other themes. These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains; sustainability related to soil conservation practices and farm labor; and delivery of emergency humanitarian aid following a disaster. In this paper, we introduce the ontology that acts as the schema for KnowWhereGraph. This broad overview provides insight into the requirements and design specifications for the graph and its schema, including the development methodology (modular ontology modeling) and the resources utilized to implement, materialize, and deploy KnowWhereGraph with its end-user interfaces and public query SPARQL endpoint.