Year: 2026
Type: conference-paper
Abstract: Large language models (LLMs) have recently advanced knowledge graph question answering (KGQA), but current methods tend to rely on LLM-induced type systems with inconsistent granularity, or perform multi-hop reasoning without explicit target-type constraints.We introduce OntGQA, a type-constrained KGQA framework that reasons over a relation-centric ontology graph, where each relation is labeled with its head and tail entity types to provide a stable schema backbone.Built on this graph, OntGQA adopts a planner-judge architecture with generative backoff: a type planner proposes plausible head-tail type pairs, a judge verifies retrieved candidates and their paths, and a generator is invoked only when all candidates are rejected.By constraining both endpoints of reasoning in type space, Ont-GQA achieves state-of-the-art performance and produces ontology-grounded reasoning chains, with substantial Hit@1 gains (87.7%→91.5% on WebQSP and 67.6%→74.6% on CWQ).
Institution: National University of Defense Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166844250