This article presents the implementation of a neuro-symbolic system within the Open Research Knowledge Graph (ORKG), a platform designed to collect and organize scientific knowledge in a structured, machine-readable format. Our approach leverages the strengths of symbolic knowledge representation to encode complex relationships and domain-specific rules, combined with the pattern recognition capabilities of neural networks to process large volumes of unstructured data, in particular scientific articles in the form of narrative text documents. With the ORKG, we developed a hybrid system that integrates a knowledge graph (symbolic system) with neural networks (subsymbolic system), enabling the machine-assisted extraction of scientific knowledge from research papers and symbolic knowledge representation, curation, organisation, and reuse. The implementation involves constructing a symbolic knowledge representation of the ORKG, training neural models on annotated datasets, and designing algorithms to synergize the two systems. Our findings suggest that neuro-symbolic integration enhances the ORKG’s ability to support complex queries, infer new relationships, and provide more robust and explainable AI-driven insights. This research contributes to the broader field of AI by showcasing the practical benefits of combining neural and symbolic methodologies in knowledge management systems.