Abstract
Natural-language access to RDF knowledge graphs is a core Semantic Web ambition. Large language models (LLMs) have advanced Text-to-SPARQL, yet on unfamiliar graphs they often generate valid queries that misrepresent the populated data model. QRAKEN is a training-free, ontology-agnostic neurosymbolic pipeline grounding generation in empirical graph evidence rather than schema expectations. An offline distiller produces TTQL, a compact description of populated multi-hop patterns, conditional frequencies and path-conditioned literal examples, plus a class-property co-occurrence matrix. Online, TTQL guides the LLM, while deterministic syntax, vocabulary and data-model checks provide diagnostics for iterative refinement. On CK25 (First International Text2SPARQL Challenge), under matched-condition recomputation on a QLever snapshot, QRAKEN achieves strict F1 of 0.643 $\pm$ 0.026 with GPT-4.1 mini and 0.652 $\pm$ 0.012 with GPT-5.4: relative gains of 30% and 32% over the strongest recomputed participant, outperforming systems using the same base model family. Ablations identify TTQL patterns as the dominant driver (+0.31 strict F1 over a shape-only baseline); the refinement loop provides a cheap safety net, rejecting triple patterns unsupported by the co-occurrence matrix. Compared with auto-derived SHACL, TTQL yields 64% higher strict F1, supporting the value of empirical patterns beyond schema exposure. With two local 35B 4-bit open-weight models at zero marginal cost, the same pipeline matches the strongest recomputed participant, and TTQL advantages over shape-only and SHACL baselines persist. Results on a single, relatively small benchmark provide an initial empirical signal; monolithic TTQL injection on very open cross-domain graphs remains the main limitation.
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Cite this article
APA 7
Grillo, R., Klic, L., & Colavizza, G. (2026). Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing. https://omanscience.com/en/articles/natural-language-questions-as-an-interface-for-knowledge-graphs-qraken-graph-distillation-and-semantic-self-healing
MLA 9
Grillo, Remo, et al. "Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing." https://omanscience.com/en/articles/natural-language-questions-as-an-interface-for-knowledge-graphs-qraken-graph-distillation-and-semantic-self-healing.
Chicago (author–date)
Grillo, Remo, Lukas Klic, and Giovanni Colavizza. 2026. "Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing." https://omanscience.com/en/articles/natural-language-questions-as-an-interface-for-knowledge-graphs-qraken-graph-distillation-and-semantic-self-healing.
Harvard
Grillo, R., Klic, L. and Colavizza, G. (2026) 'Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing', Available at: https://omanscience.com/en/articles/natural-language-questions-as-an-interface-for-knowledge-graphs-qraken-graph-distillation-and-semantic-self-healing.
Vancouver
Grillo R, Klic L, Colavizza G. Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing. https://omanscience.com/en/articles/natural-language-questions-as-an-interface-for-knowledge-graphs-qraken-graph-distillation-and-semantic-self-healing
IEEE
R. Grillo, L. Klic, and G. Colavizza, "Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing," https://omanscience.com/en/articles/natural-language-questions-as-an-interface-for-knowledge-graphs-qraken-graph-distillation-and-semantic-self-healing.