\section{Conclusion}\label{sec:conclusion}
\begin{comment}
- Revisit the research objectives and highlight the contributions of the paper.
- Emphasize the significance of introducing noise in benchmark datasets for advancing research in neurosymbolic ontology reasoners.
- Provide closing remarks and suggest directions for future work.
\end{comment}

This paper presents NSORN (Neurosymbolic Ontology Reasoning with Noise), a reproducible and extensible framework for generating noisy benchmark datasets, with a particular focus on controlled perturbations of ABox assertions. We developed three techniques for introducing noise into the ABox: logical noise, statistical noise, and random noise. Logical noise is introduced by contradicting disjoint axioms or violating domain/range constraints of object properties. Statistical noise, on the other hand, is introduced using Graph Neural Networks, which add new links with low probability scores to mimic biases commonly found in automated KG construction pipelines. Random noise involves unpredictable, arbitrarily alternations to ABox assertions. These methods were designed to evaluate the robustness and performance of ontology-based neurosymbolic reasoners under various noise conditions.

We evaluated the performance of existing neurosymbolic reasoners on \textit{Pizza}, \textit{Family} and \textit{OWL2Bench} under various noise types and levels. The resulting benchmarks were tested on state-of-the-art neurosymbolic reasoners, \textit{Box2EL} and \textit{OWL2Vec*}, as well as a purely neural method, \textit{R-GCN}. The reasoning tasks considered include membership and object property assertions, with the aim of evaluating how effectively these reasoners handle noise. 
Our experimental results show how different modeling paradigms respond to varying types and levels of noise. In our experimental setup, the GNN-based model (R-GCN) exhibited greater empirical robustness to injected perturbations, particularly under logically inconsistent noise, while embedding-based neurosymbolic models showed more pronounced degradation. In contrast to many prior studies that focus primarily on ontology completion tasks, our emphasis is on ontology reasoning under noisy conditions, where inference quality is directly evaluated. The source code of NSORN is available at \url{https://github.com/jloe2911/NoisyBench} under MIT License. \\

Gunjan Singh and Raghava Mutharaju would like to acknowledge the partial support of the Infosys Centre for Artificial Intelligence (CAI), IIIT-Delhi, India, in this work.