A Hybrid, Neuro-symbolic Approach for Scholarly Knowledge Organization

Abstract

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.