■Bibliographic Information
Xinjie Zhao, Moritz Blum, Fan Gao, Yingjian Chen, Boming Yang, Luis Marquez-Carpintero, Mónica Pina-Navarro, Yanran Fu, So Morikawa, Yusuke Iwasawa, Yutaka Matsuo, Chanjun Park and Irene Li
“AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbots”. (CIKM 2025)
◾️Overview
Large Language Models (LLMs) have demonstrated capabilities across various applications but face challenges such as hallucination, limited Large Language Models (LLMs) have demonstrated capabilities across various applications but face challenges such as hallucination, limited reasoning abilities, and factual inconsistencies, especially when tackling complex, domain-specific tasks like question answering (QA). While Knowledge Graphs (KGs) have been shown to help mitigate these issues, research on the integration of LLMs with background KGs remains limited. We introduce AGENTiGraph (Adaptive AGENTiGraph (Adaptive Generative ENgine for Task-based Interaction and Graphical Representation), a platform for knowledge management through natural language It integrates knowledge extraction, integration, and real-time visualization. AGENTiGraph employs a multi-agent architecture to dynamically interpret user intents, manage tasks, and integrate new knowledge, ensuring adaptability to evolving user requirements and data contexts Our approach demonstrates superior performance in knowledge graph interactions, especially for complex domain-specific tasks. Experimental results on a dataset of 3,500 test cases show AGENTiGraph significantly outperforms state-of-the-art zero-shot baselines, achieving 95.12% accuracy User studies corroborate its effectiveness in real-world scenarios. versatility, we extended AGENTiGraph to legislation and healthcare domains, constructing specialized KGs capable of answering complex queries in legal and medical contexts.
■Bibliographic Information
Fan Gao, Xinjie Zhao, Ding Xia, Zhongyi Zhou, Rui Yang, Jinghui Lu, Hang Jiang, Chanjun Park and Irene Li
“HealthGenie: An Interactive Knowledge-Driven LLM Framework for Tailored Dietary Guidance”. (CIKM 2025)
◾️Overview
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 In this paper, we present HealthGenie, an interactive system that combines the strengths of LLMs and KGs to provide Upon receiving a user 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. Our evaluation, comprising a within-subject comparative experiment and an open-ended discussion, demonstrates that HealthGroup’s recommendations are based on a set of preferences. 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 These findings highlight the potential of LLM-KG integration in supporting decision -We examine the system’s usefulness and effectiveness with an N=12 within-subject study and provide design considerations for the system. 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.
◾️Demo Link: https://shorturl.at/ujPK1