mlsanityData to 7 Oct 2026

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

arXiv 2502.15005 ↗First posted 20 Feb 2025cs.CL cs.AI cs.HCcited by 0

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 12 of its 28 references that are in the map. First posted on arXiv 20 Feb 2025.

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12 of 28 references.

References · 12

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,404
  3. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  4. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  5. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  6. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  7. 2022OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and conceptscited by 166
  8. 2018A Web-scale system for scientific knowledge explorationcited by 39
  9. 2024EfficientRAG: Efficient Retriever for Multi-Hop Question Answeringcited by 24
  10. 2024A Survey on Knowledge Organization Systems of Research Fields: Resources and Challengescited by 9
  11. 2024From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AIcited by 7
  12. 2016Sources of Change for Modern Knowledge Organization Systemscited by 1

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  3. 2026Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems
  4. 2025Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review
  5. 2025RAG-Anything: All-in-One RAG Framework
  6. 2025KIMAs: A Configurable Knowledge Integrated Multi-Agent System
  7. 2024OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models
  8. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey