A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems

TL;DR

This paper introduces a RAG-based agent that uses Socratic dialogue to translate natural language research queries into semantic entities, bridging detailed academic taxonomies with large bibliometric data for improved research discovery.

Contribution

It presents a novel combination of RAG and Socratic dialogue to connect user queries with Knowledge Organization Systems, enhancing accessibility of complex research taxonomies.

Findings

Demonstrates a prototype called CollabNext for research topic exploration.

Highlights focus on HBCUs and emerging researchers to improve visibility.

Shows potential for broad application in academic knowledge discovery.

Abstract

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.