Codiglione, MatteoRemondino, FabioCampana, StefanoFerdani, DanieleGraf, HolgerGuidi, GabrieleHegarty, ZackaryPescarin, SofiaRemondino, Fabio2025-09-052025-09-052025978-3-03868-277-6https://doi.org/10.2312/dh.20253280https://diglib.eg.org/handle/10.2312/dh20253280Point clouds are an asset in a wide range of applications, including heritage monitoring and conservation. However, querying capabilities over such 3D datasets, a fundamental task for their practical usability, is often highly challenging. In particular, difficulties arise when it comes to natural language querying, which can be in turn highly useful in lowering the technical barrier in inspecting 3D point clouds. The present paper explores various approaches for natural language querying over point clouds, leaning for the SPARQL-based query system allowed by the 3DOnt framework for point clouds management, recasting the problem as a natural language to SPARQL translation. After reviewing existing methodologies, we propose NL-2- SPARQL, a novel and flexible neuro-symbolic approach that integrates a Large Language Model (LLM) with a Graph Exploration and Query Building tool (GEQB). We then evaluate this method and demonstrate its application within the 3DOnt framework, highlighting its broader applicability to knowledge graphs in general, beyond this specific 3D-oriented context. A video presentation of the 3DOnt framework is available at https://3dom.fbk.eu/projects/3DOnt.Attribution 4.0 International LicenseOntology, point clouds, LLM, SPARQL, natural language query, NL, Neuro-symbolicOntologypoint cloudsLLMSPARQLnatural language queryNLNeurosymbolic10.2312/dh.2025328010 pages