TL;DR
An approach that translates SPARQL queries to the Notation3 (N3) SW rule language, where each SPARQL query is translated into a single N3 rule in order to improve the exchangeability of translated rules.
Building automation systems are increasingly represented as semantic knowledge graphs (KGs) using ontologies such as Brick and ASHRAE 223P, creating a machine-readable substrate for artificial-intelligence applications. One promising application is translating natural-language questions into SPARQL (text-to-SPARQL), wh...
It is shown that for database queries, data model concepts pass to LLMs more effectively through representations whose vocabulary terms carry declared, machine-readable semantics (controlled semantics).
Preliminary experiments with the proof-of-concept prototype suggest that querying graph data with ontological knowledge may finally be within reach.
Two SRL engines were implemented and evaluated on classical RDF reasoning tasks for soundness, completeness, and speed, finding a usable SRL engine can be built inexpensively on top of a SPARQL engine, with a moderate speed trade-off that a dedicated implementation recovers.
This work constructs a rewriting which takes as input both shapes and an OWL EL$^-$ ontology -- a fragment of OWL EL restricting the usage of existential restrictions -- and produces SHACL constraints, providing a powerful tool which simplifies combining reasoning with validation.
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