NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning

• URL: http://arxiv.org/abs/2607.15776v1
• Date: Fri, 17 Jul 2026 09:12:10 GMT
• Title: NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning
• Abstract summary: We focus on a fundamental subsumption reasoning problem.<n>We propose NeurOWL, an end-to-end neurosymbolic framework.<n>We evaluate NeurOWL on realworld- NeurOWL across multiple domains.
• Score: 15.085939995703667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: OWL ontologies provide a formal knowledge representation framework that enables semantic reasoning, and have been widely adopted across domains such as healthcare and bioinformatics. In practice, however, real-world ontologies are often incomplete, which pose challenges for reasoning. In this work, we focus on a fundamental subsumption reasoning problem: given an incomplete ontology and a candidate (non-entailed) subsumption, determine whether the subsumption is semantically plausible and, if so, providing a logically sound explanation containing potential missing axioms. This task unifies subsumption verification with ontology abduction, and generalizes the latter by removing the need for a predefined candidate set of missing axioms. To address this subsumption reasoning problem, we propose NeurOWL, an end-to-end neuro-symbolic framework that jointly performs verification and abduction, leveraging both formally defined semantics and textual semantics through Large Language Models and ontology embeddings. We evaluate NeurOWL on real-world ontologies across multiple domains, demonstrating strong and robust performance across different domains.

Related papers

• The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes [55.42597391158023]
  This survey provides a systematic analysis of more than 300 recent papers from arXiv, Semantic Scholar, Google Scholar, Papers with Code, and the ACL Anthology.<n>We introduce a structured taxonomy of reasoning research, covering Chain-of-Thought reasoning, multi-hop reasoning, mathematical reasoning, common sense reasoning, visual and temporal reasoning, code and algorithmic reasoning, retrieval-augmented reasoning, tool-augmented and agentic reasoning, and reinforcement learning-based reasoning.<n>We synthesize recurring limitations and failure modes, such as reasoning hallucinations, brittle multi-step inference, weak causal abstraction, and poor cross
  arXiv  Detail & Related papers  (2026-06-09T21:59:37Z)
• Grounding vs. Compositionality: On the Non-Complementarity of Reasoning in Neuro-Symbolic Systems [0.0]
  A central assumption in neuro-symbolic AI is that compositional reasoning will emerge as a byproduct of successful symbol grounding.<n>This work presents the first systematic empirical analysis to challenge this assumption by disentangling the contributions of grounding and reasoning.
  arXiv  Detail & Related papers  (2026-04-29T10:41:30Z)
• Hybrid Models for Natural Language Reasoning: The Case of Syllogistic Logic [3.421904493396495]
  We investigate the logical generalization capabilities of pre-trained large language models (LLMs) using the syllogistic fragment as a benchmark.<n>We propose a hybrid architecture integrating symbolic reasoning with neural computation.<n>Our experiments show that high efficiency is preserved even with relatively small neural components.
  arXiv  Detail & Related papers  (2025-10-10T15:27:29Z)
• A Survey on Latent Reasoning [100.54120559169735]
  Large Language Models (LLMs) have demonstrated impressive reasoning capabilities.<n>CoT reasoning that verbalizes intermediate steps limits the model's expressive bandwidth.<n>Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state.
  arXiv  Detail & Related papers  (2025-07-08T17:29:07Z)
• Modal Logic for Stratified Becoming: Actualization Beyond Possible Worlds [55.2480439325792]
  This article develops a novel framework for modal logic based on the idea of stratified actualization.<n>Traditional Kripke semantics treat modal operators as quantification over fully determinate alternatives.<n>We propose a system Stratified Actualization Logic (SAL) in which modalities are indexed by levels of ontological stability, interpreted as admissibility.
  arXiv  Detail & Related papers  (2025-06-12T18:35:01Z)
• LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning [74.0242521818214]
  This paper systematically investigates the comparative dynamics of inductive (System 1) versus abductive/deductive (System 2) inference in large language models (LLMs)<n>We utilize a controlled analogical reasoning environment, varying modality (textual, visual, symbolic), difficulty, and task format (MCQ / free-text)<n>Our analysis reveals System 2 pipelines generally excel, particularly in visual/symbolic modalities and harder tasks, while System 1 is competitive for textual and easier problems.
  arXiv  Detail & Related papers  (2025-02-16T15:54:53Z)
• TransBox: EL++-closed Ontology Embedding [14.850996103983187]
  We develop an effective EL++-closed embedding method that can handle many-to-one, one-to-many and many-to-many relations.<n>Our experiments demonstrate that TransBox achieves state-of-the-art performance across various real-world datasets for predicting complex axioms.
  arXiv  Detail & Related papers  (2024-10-18T16:17:10Z)
• The Mercurial Top-Level Ontology of Large Language Models [0.0]
  We systematize and analyze implicit ontological commitments in the responses generated by large language models (LLMs) We investigate how LLMs exhibit implicit ontological categorizations that are reflected in the texts they generate.
  arXiv  Detail & Related papers  (2024-04-26T16:52:32Z)
• Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation [85.12429517510311]
  LogicDiag is a neural-logic semi-supervised learning framework for semantic segmentation. Our key insight is that conflicts within pseudo labels, identified through symbolic knowledge, can serve as strong yet commonly ignored learning signals. We showcase the practical application of LogicDiag in the data-hungry segmentation scenario, where we formalize the structured abstraction of semantic concepts as a set of logic rules.
  arXiv  Detail & Related papers  (2023-08-24T06:50:07Z)
• 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)
• Dual Box Embeddings for the Description Logic EL++ [16.70961576041243]
  Similar to Knowledge Graphs (KGs), Knowledge Graphs are often incomplete, and maintaining and constructing them has proved challenging. Similar to KGs, a promising approach is to learn embeddings in a latent vector space, while additionally ensuring they adhere to the semantics of the underlying DL. We propose a novel ontology embedding method named Box$2$EL for the DL EL++, which represents both concepts and roles as boxes.
  arXiv  Detail & Related papers  (2023-01-26T14:13:37Z)
• Logical Neural Networks [51.46602187496816]
  We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning) Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a highly intepretable disentangled representation. Inference is omni rather than focused on predefined target variables, and corresponds to logical reasoning.
  arXiv  Detail & Related papers  (2020-06-23T16:55:45Z)

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.