# HealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models

arXiv: https://arxiv.org/abs/2504.14594 · HTML: https://mlsanity.com/paper/2504.14594/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 75 references that are in the map.
- First posted on arXiv 20 Apr 2025.

## Builds on (most cited first, 19 of 19)

- [Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph](https://mlsanity.com/paper/2307.07697/) (2023)
- [KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph](https://mlsanity.com/paper/2402.11163/) (2024)
- [Graphologue: Exploring Large Language Model Responses with Interactive Diagrams](https://mlsanity.com/paper/2305.11473/) (2023)
- [Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering](https://mlsanity.com/paper/2404.14741/) (2024)
- [Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation](https://mlsanity.com/paper/2501.02226/) (2025)
- [Beyond the Chat: Executable and Verifiable Text-Editing with LLMs](https://mlsanity.com/paper/2309.15337/) (2023)
- [ComPeer: A Generative Conversational Agent for Proactive Peer Support](https://mlsanity.com/paper/2407.18064/) (2024)
- [ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework](https://mlsanity.com/paper/2403.00781/) (2024)
- [KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation](https://mlsanity.com/paper/2107.12548/) (2021)
- [NutriBench: A Dataset for Evaluating Large Language Models on Nutrition Estimation from Meal Descriptions](https://mlsanity.com/paper/2407.12843/) (2024)
- [KNOWNET: Guided Health Information Seeking from LLMs via Knowledge Graph Integration](https://mlsanity.com/paper/2407.13598/) (2024)
- [Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcare](https://mlsanity.com/paper/2211.16444/) (2022)
- [Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities](https://mlsanity.com/paper/2304.01311/) (2023)
- [A Preliminary Roadmap for LLMs as Assistants in Exploring, Analyzing, and Visualizing Knowledge Graphs](https://mlsanity.com/paper/2404.01425/) (2024)
- [AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data](https://mlsanity.com/paper/2410.11531/) (2024)
- [Patterns for Representing Knowledge Graphs to Communicate Situational Knowledge of Service Robots](https://mlsanity.com/paper/2101.10602/) (2021)
- [Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain](https://mlsanity.com/paper/2309.16134/) (2023)
- [FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data](https://mlsanity.com/paper/2412.02784/) (2024)
- [Learnings from a Large-Scale Deployment of an LLM-Powered Expert-in-the-Loop Healthcare Chatbot](https://mlsanity.com/paper/2409.10354/) (2024)

## Built on by (most cited first, 4 of 4)

- [InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios](https://mlsanity.com/paper/2509.22502/) (2025)
- [DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs](https://mlsanity.com/paper/2601.20311/) (2026)
- [FAM-Bench: A Multimodal Benchmark for Condition-Aware Food-as-Medicine Reasoning](https://mlsanity.com/paper/2605.31410/) (2026)
- [Nutri-ATLAS: Embodied Agent for Tabulated Lookup and Assistance for Smarter nutrition](https://mlsanity.com/paper/2609.32803/) (2026)

## Abstract (arXiv)

Seeking dietary guidance often requires navigating complex professional knowledge while accommodating individual health conditions. Knowledge Graphs (KGs) offer structured and interpretable nutritional information, whereas Large Language Models (LLMs) naturally facilitate conversational recommendation delivery. In this paper, we present HealthGenie, an interactive system that combines the strengths of LLMs and KGs to provide personalized dietary recommendations along with hierarchical information visualization for a quick and intuitive overview. Upon receiving a user query, HealthGenie performs query refinement and retrieves relevant information from a pre-built KG. The system then visualizes and highlights pertinent information, organized by defined categories, while offering detailed, explainable recommendation rationales. Users can further tailor these recommendations by adjusting preferences interactively. Our evaluation, comprising a within-subject comparative experiment and an open-ended discussion, demonstrates that HealthGenie effectively supports users in obtaining personalized dietary guidance based on their health conditions while reducing interaction effort and cognitive load. These findings highlight the potential of LLM-KG integration in supporting decision-making through explainable and visualized information. We examine the system's usefulness and effectiveness with an N=12 within-subject study and provide design considerations for future systems that integrate conversational LLM and KG.