In this talk, we delve into the cutting-edge realm of Neuro-Symbolic Artificial Intelligence (AI), focusing on the synergistic integration of Knowledge Graphs and Generative AI such as Large Language Models. Neuro-Symbolic AI represents a transformative approach that combines the robust, interpretable reasoning capabilities of symbolic AI with the adaptive, data-driven strengths of neural networks. The talk will illuminate how this fusion offers a promising pathway towards more intelligent, explainable, and reliable AI systems. As a showcase of our approach towards neuro-symbolic AI we will demonstrate Corporate Memory, an enterprise ready Knowledge Graph and Neuro-Symbolic AI platform used by major Enterprises as well as the Open Research Knowledge Graph. The ORKG is representing research contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques leveraging Large Language Models. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for enterprises and researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches.