Knowledge Graph
Knowledge graphs (KGs) are structured representations of information, aiming to organize data into interconnected entities and relationships to facilitate knowledge discovery and reasoning. Current research heavily focuses on integrating KGs with large language models (LLMs) to enhance question answering, knowledge graph completion, and other knowledge-intensive tasks, often employing retrieval-augmented generation (RAG) and graph neural network architectures. This integration improves the accuracy and efficiency of various applications, ranging from legal article recommendation and medical diagnosis to supporting legislative processes and scholarly research. The resulting advancements have significant implications for diverse fields requiring complex information processing and reasoning.
Papers
Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring
Hasan Abu-Rasheed, Mohamad Hussam Abdulsalam, Christian Weber, Madjid Fathi
Reinforcement Learning for Conversational Question Answering over Knowledge Graph
Mi Wu
keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM
Chaojie Wang, Yishi Xu, Zhong Peng, Chenxi Zhang, Bo Chen, Xinrun Wang, Lei Feng, Bo An
FusionMind -- Improving question and answering with external context fusion
Shreyas Verma, Manoj Parmar, Palash Choudhary, Sanchita Porwal
HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses
Xinke Jiang, Ruizhe Zhang, Yongxin Xu, Rihong Qiu, Yue Fang, Zhiyuan Wang, Jinyi Tang, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang
KnowledgeNavigator: Leveraging Large Language Models for Enhanced Reasoning over Knowledge Graph
Tiezheng Guo, Qingwen Yang, Chen Wang, Yanyi Liu, Pan Li, Jiawei Tang, Dapeng Li, Yingyou Wen