@inproceedings{bian-etal-2026-query,
title = "From Query to Logic: Ontology-Driven Multi-Hop Reasoning in {LLM}s",
author = "Bian, Haonan and
Qi, Yutao and
Yang, Rui and
Che, Yuanxi and
Wang, Jiaqian and
Xia, Heming and
Zhen, Ranran",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.687/",
pages = "14035--14051",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs), despite their success in question answering, exhibit limitations in complex multi-hop question answering (MQA) tasks that necessitate non-linear, structured reasoning. This limitation stems from their inability to adequately capture deep conceptual relationships between entities. To overcome this challenge, we present ORACLE (Ontology-driven Reasoning And Chain for Logical Elucidation), a training-free framework that combines LLMs' generative capabilities with the structural benefits of knowledge graphs. Our approach operates through three stages: (1) dynamic construction of question-specific knowledge ontologies using LLMs, (2) transformation of these ontologies into First-Order Logic (FOL) reasoning chains, and (3) systematic decomposition of the original query into logically coherent sub-questions. Extensive experiments across a diverse set of models and standard MQA benchmarks demonstrate that our framework achieves competitive performance while producing more interpretable reasoning chains."
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<abstract>Large Language Models (LLMs), despite their success in question answering, exhibit limitations in complex multi-hop question answering (MQA) tasks that necessitate non-linear, structured reasoning. This limitation stems from their inability to adequately capture deep conceptual relationships between entities. To overcome this challenge, we present ORACLE (Ontology-driven Reasoning And Chain for Logical Elucidation), a training-free framework that combines LLMs’ generative capabilities with the structural benefits of knowledge graphs. Our approach operates through three stages: (1) dynamic construction of question-specific knowledge ontologies using LLMs, (2) transformation of these ontologies into First-Order Logic (FOL) reasoning chains, and (3) systematic decomposition of the original query into logically coherent sub-questions. Extensive experiments across a diverse set of models and standard MQA benchmarks demonstrate that our framework achieves competitive performance while producing more interpretable reasoning chains.</abstract>
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%0 Conference Proceedings
%T From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs
%A Bian, Haonan
%A Qi, Yutao
%A Yang, Rui
%A Che, Yuanxi
%A Wang, Jiaqian
%A Xia, Heming
%A Zhen, Ranran
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F bian-etal-2026-query
%X Large Language Models (LLMs), despite their success in question answering, exhibit limitations in complex multi-hop question answering (MQA) tasks that necessitate non-linear, structured reasoning. This limitation stems from their inability to adequately capture deep conceptual relationships between entities. To overcome this challenge, we present ORACLE (Ontology-driven Reasoning And Chain for Logical Elucidation), a training-free framework that combines LLMs’ generative capabilities with the structural benefits of knowledge graphs. Our approach operates through three stages: (1) dynamic construction of question-specific knowledge ontologies using LLMs, (2) transformation of these ontologies into First-Order Logic (FOL) reasoning chains, and (3) systematic decomposition of the original query into logically coherent sub-questions. Extensive experiments across a diverse set of models and standard MQA benchmarks demonstrate that our framework achieves competitive performance while producing more interpretable reasoning chains.
%U https://aclanthology.org/2026.findings-acl.687/
%P 14035-14051
Markdown (Informal)
[From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs](https://aclanthology.org/2026.findings-acl.687/) (Bian et al., Findings 2026)
ACL
- Haonan Bian, Yutao Qi, Rui Yang, Yuanxi Che, Jiaqian Wang, Heming Xia, and Ranran Zhen. 2026. From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 14035–14051, San Diego, California, United States. Association for Computational Linguistics.