@inproceedings{shan-etal-2026-reasoning,
title = "Reasoning with Ontology Graph: Toward Type-Constrained Knowledge Graph Question Answering",
author = "Shan, Yongxue and
Peng, Jie and
Dong, Zixuan and
Hu, Fei and
Wang, Xiaodong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.489/",
pages = "10684--10697",
ISBN = "979-8-89176-390-6",
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, OntGQA achieves state-of-the-art performance and produces ontology-grounded reasoning chains, with substantial Hit@1 gains (87.7{\%}{\textrightarrow}91.5{\%} on WebQSP and 67.6{\%}{\textrightarrow}74.6{\%} on CWQ)."
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%0 Conference Proceedings
%T Reasoning with Ontology Graph: Toward Type-Constrained Knowledge Graph Question Answering
%A Shan, Yongxue
%A Peng, Jie
%A Dong, Zixuan
%A Hu, Fei
%A Wang, Xiaodong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F shan-etal-2026-reasoning
%X 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, OntGQA 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).
%U https://aclanthology.org/2026.acl-long.489/
%P 10684-10697
Markdown (Informal)
[Reasoning with Ontology Graph: Toward Type-Constrained Knowledge Graph Question Answering](https://aclanthology.org/2026.acl-long.489/) (Shan et al., ACL 2026)
ACL