Raghava Mutharaju

In the field of neurosymbolic computing, systematic evaluation of ontology reasoning systems under noisy conditions remains underexplored. In particular, there is a need for benchmark frameworks that enable reproducible assessment of how neurosymbolic reasoners behave when ontological data are corrupted. Thus, this work aims to develop a mechanism for introducing noise into an ontology, particularly focusing on the Assertional Box, and evaluate the performance of existing neurosymbolic reasoners on commonly used ontologies under varying levels of noise. We developed NSORN (Neurosymbolic Ontology Reasoning with Noise), a first-step benchmark framework that consists of three techniques to introduce noise into ontologies: logical, statistical, and random noise. Logical noise uses logical violations of disjoint axioms and domain/range constraints. While random noise corrupts existing triples by replacing either the subject or object of a triple with a random entity, statistical noise is introduced using graph neural networks to add noisy facts with low-probability scores. We evaluated the performance of existing neurosymbolic reasoners by introducing noise to Pizza , Family , and OWL2Bench ontologies under these noise types with various levels. The resulting benchmarks were tested on two state-of-the-art neurosymbolic reasoners, Box2EL and OWL2Vec* , as well as a purely neural method, R-GCN . We focus on reasoning tasks, such as class membership and object property assertions, to test how these reasoners handle noise. In our experimental setup, R-GCN consistently outperforms Box2EL and OWL2Vec* in robustness to noisy ontological data—even under the most destructive form, logical noise—maintaining stable membership and object property assertion performance while embedding-based models degrade sharply.

Recently, there has been significant progress in the development of robust and highly scalable neurosymbolic description logic reasoners. However, the field faces challenges arising from diverse design strategies and evaluation methods. We address the latter challenge by emphasizing the critical requirement for a comprehensive benchmark framework tailored to the unique evaluation needs of neurosymbolic description logic reasoners. In this paper, we address barriers that must be overcome to facilitate the effective evaluation of these reasoners and outline a potential methodology for designing the benchmark framework. This work contributes toward a more systematic and principled evaluation framework for neurosymbolic reasoning, highlighting the broader role of benchmarks in advancing the field.

Applied ethics is ubiquitous in most domains, requiring much deliberation due to its philosophical nature. Varying views often lead to conflicting courses of action where ethical dilemmas become challenging to resolve. Although many factors contribute to such a decision, the major driving forces can be discretized and thus simplified to provide an indicative answer. Knowledge representation and reasoning offer a way to explicitly translate abstract ethical concepts into applicable principles within the context of an event. To achieve this, we propose ApplE, an Applied Ethics ontology that captures philosophical theory and event context to holistically describe the morality of an action. The development process adheres to a modified version of the Simplified Agile Methodology for Ontology Development (SAMOD) and utilizes standard design and publication practices. Using ApplE, we model a use case from the bioethics domain that demonstrates our ontology's social and scientific value. Apart from the ontological reasoning and quality checks, ApplE is also evaluated using the three-fold testing process of SAMOD. ApplE follows FAIR principles and aims to be a viable resource for applied ethicists and ontology engineers.