A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems
This addresses the challenge of connecting intuitive user queries with structured knowledge systems, particularly for enhancing visibility in science systems, though it appears incremental as it builds on existing RAG and dialogue methods.
The paper tackles the problem of mapping natural language queries about research topics to precise semantic entities by proposing a Retrieval Augmented Generation (RAG) agent combined with Socratic dialogue, aiming to bridge domain-specific knowledge organization systems with broad bibliometric repositories to make academic taxonomies more accessible.
In this paper, we propose a Retrieval Augmented Generation (RAG) agent that maps natural language queries about research topics to precise, machine-interpretable semantic entities. Our approach combines RAG with Socratic dialogue to align a user's intuitive understanding of research topics with established Knowledge Organization Systems (KOSs). The proposed approach will effectively bridge "little semantics" (domain-specific KOS structures) with "big semantics" (broad bibliometric repositories), making complex academic taxonomies more accessible. Such agents have the potential for broad use. We illustrate with a sample application called CollabNext, which is a person-centric knowledge graph connecting people, organizations, and research topics. We further describe how the application design has an intentional focus on HBCUs and emerging researchers to raise visibility of people historically rendered invisible in the current science system.