The rapid development of generative AI leveraging neural models, particularly with the introduction of large language models (LLMs), has fundamentally advanced natural language processing and generation. However, such neural models are non-deterministic, opaque, and tend to confabulate. Knowledge Graphs (KGs) on the other hand contain factual information represented in a symbolic way for humans and machines following formal knowledge representation formalisms. However, the creation and curation of KGs is time-consuming, cumbersome, and resource-demanding. A key research challenge now is how to synergistically combine both formalisms with the human in the loop (Hybrid AI) to obtain structured and machine-processable knowledge in a scalable way. We introduce an approach for a tight integration of Humans, Neural Models (LLM), and Symbolic Representations (KG) for the semiautomatic creation and curation of Scholarly Knowledge Graphs. Our approach, while demonstrated in the scholarly context, establishes generalizable principles for neuro-symbolic integration that can be adapted to other domains. We implement and integrate our approach comprising an intelligent user interface and prompt templates for interaction with an LLM in the Open Research Knowledge Graph. We perform a thorough analysis of our approach and implementation with a user evaluation to assess the merits of the neuro-symbolic, hybrid approach for organizing scholarly knowledge.

Despite improved digital access to scholarly knowledge in recent decades, scholarly communication remains exclusively document-based. In this form, scholarly knowledge is hard to process automatically. We present the first steps towards a knowledge graph based infrastructure that acquires scholarly knowledge in machine actionable form thus enabling new possibilities for scholarly knowledge curation, publication and processing. The primary contribution is to present, evaluate and discuss multi-modal scholarly knowledge acquisition, combining crowdsourced and automated techniques. We present the results of the first user evaluation of the infrastructure with the participants of a recent international conference. Results suggest that users were intrigued by the novelty of the proposed infrastructure and by the possibilities for innovative scholarly knowledge processing it could enable.