Evaluating Neuro-Symbolic AI Architectures: Design Principles, Qualitative Benchmark, Comparative Analysis and Results

Oualid BOUGZIME, Samir JABBAR, Christophe Cruz, Frédéric DEMOLY

Keywords: Neuro-symbolic Artificial Intelligence, Neural Network, Symbolic AI, Multi-agent systems (MAS), Ontology, Knowledge graph, Benchmark

TL;DR: We define the design principles of neuro-symbolic AI architectures, evaluate their performance across key criteria, and present an ontology application in the field of 4D printing.

Track: Knowledge Graphs, Ontologies and Neurosymbolic AI

Abstract: Neuro-symbolic artificial intelligence (NSAI) represents a transformative approach in artificial intelligence (AI) by combining deep learning's ability to handle large-scale and unstructured data with the structured reasoning of symbolic methods. By leveraging their complementary strengths, NSAI enhances generalization, reasoning, and scalability while addressing key challenges such as transparency and data efficiency. This paper systematically studies diverse NSAI architectures, highlighting their unique approaches to integrating neural and symbolic components. This study then evaluates these architectures against comprehensive set of criteria, including generalization, reasoning capabilities, transferability, and interpretability, therefore providing a comparative analysis of their respective strengths and limitations. Notably, the Neuro → Symbolic ← Neuro model consistenty outperforms its counterparts across all evaluation metrics. This result aligns with state-of-the-art research that highlight the efficacy of such architectures in harnessing advanced technologies like multi-agent systems. Moreover, our NSAI framework using retrieval-augmented illustrates how the 4D printing ontology can be systematically enriched with additional classes, object properties, data properties and individuals.

Paper Type: Long Paper

Submission Number: 13