From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs

• URL: http://arxiv.org/abs/2508.01424v1
• Date: Sat, 02 Aug 2025 16:12:42 GMT
• Title: From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs
• Authors: Haonan Bian, Yutao Qi, Rui Yang, Yuanxi Che, Jiaqian Wang, Heming Xia, Ranran Zhen,
• Abstract summary: We present **ORACLE** (**O**ntology-driven **R**easoning **A**nd **C**hain for **L**ogical **E**ucidation), a training-free framework that combines LLMs' generative capabilities with the structural benefits of knowledge graphs.<n> Experimental results show that our framework logically highly competitive performance, rivaling current state-of-the-art models like DeepSeek-R1.
• Score: 3.828692258888057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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** (**O**ntology-driven **R**easoning **A**nd **C**hain for **L**ogical **E**ucidation), 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 reasoning chains, and (3) systematic decomposition of the original query into logically coherent sub-questions. Experimental results on several standard MQA benchmarks show that our framework achieves highly competitive performance, rivaling current state-of-the-art models like DeepSeek-R1. Detailed analyses further confirm the effectiveness of each component, while demonstrating that our method generates more logical and interpretable reasoning chains than existing approaches.

Related papers

• Truly Assessing Fluid Intelligence of Large Language Models through Dynamic Reasoning Evaluation [75.26829371493189]
  Large language models (LLMs) have demonstrated impressive reasoning capacities that mirror human-like thinking.<n>Existing reasoning benchmarks either focus on domain-specific knowledge (crystallized intelligence) or lack interpretability.<n>We propose DRE-Bench, a dynamic reasoning evaluation benchmark grounded in a hierarchical cognitive framework.
  arXiv  Detail & Related papers  (2025-06-03T09:01:08Z)
• Learning to Reason via Mixture-of-Thought for Logical Reasoning [56.24256916896427]
  Mixture-of-Thought (MoT) is a framework that enables LLMs to reason across three complementary modalities: natural language, code, and truth-table.<n>MoT adopts a two-phase design: (1) self-evolving MoT training, which jointly learns from filtered, self-generated rationales across modalities; and (2) MoT inference, which fully leverages the synergy of three modalities to produce better predictions.
  arXiv  Detail & Related papers  (2025-05-21T17:59:54Z)
• Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLM [11.181783720439563]
  Large Language Models (LLMs) display sophisticated reasoning abilities via extended Chain-of-Thought (CoT) generation.<n>RLMs often demonstrate counterintuitive and unstable behaviors, such as performance degradation under few-shot prompting.<n>We introduce a unified graph-based analytical framework for better modeling the reasoning processes of RLMs.
  arXiv  Detail & Related papers  (2025-05-20T03:54:57Z)
• Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning Models: Capabilities and Challenges [4.668749313973097]
  This paper systematically evaluate Large Language Models (LLMs) and Large Reasoning Models (LRMs) across three levels of reasoning complexity.<n>We curate 26 challenges where models answer directly or by Python Code Interpreter.<n>LRMs show robust performance across tasks with various levels of difficulty, often competing or surpassing traditional first-principle-based methods.
  arXiv  Detail & Related papers  (2025-05-16T18:32:35Z)
• A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
  Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making.<n>With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems.<n>We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
  arXiv  Detail & Related papers  (2025-04-12T01:27:49Z)
• CLR-Fact: Evaluating the Complex Logical Reasoning Capability of Large Language Models over Factual Knowledge [44.59258397967782]
  Large language models (LLMs) have demonstrated impressive capabilities across various natural language processing tasks. We present a systematic evaluation of state-of-the-art LLMs' complex logical reasoning abilities. We find that LLMs excel at reasoning over general world knowledge but face significant challenges with specialized domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-07-30T05:40:32Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• Improving Large Language Models in Event Relation Logical Prediction [33.88499005859982]
  Event relation extraction is a challenging task that demands thorough semantic understanding and rigorous logical reasoning. In this paper, we conduct an in-depth investigation to systematically explore the capability of LLMs in understanding and applying event relation logic. Our study reveals that LLMs are not logically consistent reasoners, which results in their suboptimal performance on tasks that need rigorous reasoning.
  arXiv  Detail & Related papers  (2023-10-13T14:53:06Z)
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
  We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
  arXiv  Detail & Related papers  (2023-06-21T07:34:27Z)
• Exploring Self-supervised Logic-enhanced Training for Large Language Models [59.227222647741094]
  In this paper, we make the first attempt to investigate the feasibility of incorporating logical knowledge through self-supervised post-training. We devise an auto-regressive objective variant of MERIt and integrate it with two LLM series, i.e., FLAN-T5 and LLaMA, with parameter size ranging from 3 billion to 13 billion. The results on two challenging logical reasoning benchmarks demonstrate the effectiveness of LogicLLM.
  arXiv  Detail & Related papers  (2023-05-23T06:13:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.