用苏格拉底式对话让自然语言问题精准对接学术知识体系
A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems
- 通过苏格拉底式对话增强RAG，将用户直觉理解映射到可计算的语义实体
- 实现领域知识系统与大型文献库之间的语义对齐，提升学术分类可访问性
- 专为提升非主流研究者和历史被忽视群体可见性而设计
本文提出一种检索增强生成（RAG）代理，将关于研究主题的自然语言查询映射为精确、机器可读的语义实体。该方法结合RAG与苏格拉底式对话，将用户对研究主题的直观理解与既有的知识组织系统（KOSs）对齐。所提方法有效连接了‘小语义’（领域特定的KOS结构）与‘大语义’（广泛文献计量资源），使复杂的学术分类体系更易获取。此类代理具有广泛应用潜力。我们以一个名为CollabNext的应用为例，该应用是一个以个人为中心的知识图谱，关联人物、机构与研究主题。其应用设计特别关注历史黑人学院与大学（HBCUs）及新兴研究者，旨在提升在现有科学体系中长期被忽视的个体的可见性。
原文摘要 · Abstract (English)
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.
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