# KNOWNET: Guided Health Information Seeking from LLMs via Knowledge Graph Integration

arXiv: https://arxiv.org/abs/2407.13598 · HTML: https://mlsanity.com/paper/2407.13598/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2024.

## Built on by (most cited first, 15 of 15)

- [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025)
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- [Unequal Opportunities: Examining the Bias in Geographical Recommendations by Large Language Models](https://mlsanity.com/paper/2504.05325/) (2025)
- [HealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models](https://mlsanity.com/paper/2504.14594/) (2025)
- [RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.06056/) (2025)
- [NeuroSync: Intent-Aware Code-Based Problem Solving via Direct LLM Understanding Modification](https://mlsanity.com/paper/2508.02823/) (2025)
- [RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systems](https://mlsanity.com/paper/2601.12991/) (2026)
- [Stealing Creator's Workflow: A Creator-Inspired Agentic Framework with Iterative Feedback Loop for Improved Scientific Short-form Generation](https://mlsanity.com/paper/2504.18805/) (2025)
- [HypoChainer: A Collaborative System Combining LLMs and Knowledge Graphs for Hypothesis-Driven Scientific Discovery](https://mlsanity.com/paper/2507.17209/) (2025)
- [A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations](https://mlsanity.com/paper/2509.16454/) (2025)
- [YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery](https://mlsanity.com/paper/2509.19182/) (2025)
- [DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs](https://mlsanity.com/paper/2601.20311/) (2026)
- [Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking](https://mlsanity.com/paper/2505.01696/) (2025)
- [A phenotype-driven and evidence-governed framework for knowledge graph enrichment and hypotheses discovery in population data](https://mlsanity.com/paper/2604.16982/) (2026)
- [Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation](https://mlsanity.com/paper/2606.06869/) (2026)

## Abstract (arXiv)

The increasing reliance on Large Language Models (LLMs) for health information seeking can pose severe risks due to the potential for misinformation and the complexity of these topics. This paper introduces KNOWNET a visualization system that integrates LLMs with Knowledge Graphs (KG) to provide enhanced accuracy and structured exploration. Specifically, for enhanced accuracy, KNOWNET extracts triples (e.g., entities and their relations) from LLM outputs and maps them into the validated information and supported evidence in external KGs. For structured exploration, KNOWNET provides next-step recommendations based on the neighborhood of the currently explored entities in KGs, aiming to guide a comprehensive understanding without overlooking critical aspects. To enable reasoning with both the structured data in KGs and the unstructured outputs from LLMs, KNOWNET conceptualizes the understanding of a subject as the gradual construction of graph visualization. A progressive graph visualization is introduced to monitor past inquiries, and bridge the current query with the exploration history and next-step recommendations. We demonstrate the effectiveness of our system via use cases and expert interviews.