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GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases

A plan-guided graph retrieval method for semi-structured knowledge bases.

The paper proposes GRASP, a plan-guided graph retrieval system that uses adaptive fusion and reranking to improve the accuracy of retrieving relevant information from semi-structured knowledge bases. The approach combines a planning module with a retrieval module to adaptively select relevant subgraphs and fuse their representations. This allows for more accurate and efficient retrieval of relevant information.

Based on: GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases · arXiv

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SCPRM: A Schema-aware Cumulative Process Reward Model for Knowledge Graph Question Answering

A schema-aware cumulative process reward model for knowledge graph question answering.

The paper proposes SCPRM, a schema-aware cumulative process reward model for knowledge graph question answering. It aims to improve the performance of KGQA systems by incorporating schema information and cumulative rewards. The authors evaluate SCPRM on several benchmark datasets and demonstrate its effectiveness compared to existing methods.

Based on: SCPRM: A Schema-aware Cumulative Process Reward Model for Knowledge Graph Question Answering · arXiv

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BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration

A paper proposing a method for bootstrapping e-commerce attribute taxonomies using human-AI collaboration.

The authors present BEATS, an approach to iteratively refine e-commerce attribute taxonomies through human-AI collaboration. This method aims to improve search performance by leveraging AI-driven suggestions and human feedback. The proposed framework is designed to be applicable in various e-commerce scenarios.

Based on: BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration · arXiv

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Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization

A generative AI system integrating Retrieval-Augmented Generation, Vector Stores, and Knowledge Graphs for legal information retrieval.

The paper presents a jurisdiction-specific legal information retrieval system that combines Retrieval-Augmented Generation, Vector Stores, and Knowledge Graphs constructed via Hierarchical Non-Negative Matrix Factorization. The system is designed to enhance information retrieval and AI reasoning in the legal domain, minimizing hallucinations. It empowers AI agents to identify complex connections among cases, statutes, and legal precedents.

Based on: Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization

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How to Build an Adaptive AI Tutor for Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)

A paper introducing a novel framework for developing adaptable AI tutoring systems using knowledge graphs and retrieval-augmented generation.

The paper presents a framework called KG-RAG, which integrates structured knowledge representation with context-aware retrieval to improve AI tutoring. It addresses challenges in maintaining factual accuracy and delivering coherent instruction. The authors provide empirical validation through controlled experiments demonstrating significant learning improvements.

Based on: How to Build an Adaptive AI Tutor for Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)

HighlightAdvanced Engineering Informatics

A stepwise intelligence generative method for structured maintenance guidance documents based on knowledge graph augmented LLM

This paper proposes a method for generating structured maintenance guidance documents using knowledge graphs and large language models.

The authors present a stepwise approach to generate structured maintenance guidance documents by augmenting a knowledge graph with a large language model. This method aims to provide accurate and informative guidance documents for maintenance tasks. The proposed approach is based on the integration of knowledge graphs and large language models, which enables the generation of high-quality content.

Based on: A stepwise intelligence generative method for structured maintenance guidance documents based on knowledge graph augmented LLM · Advanced Engineering Informatics

HighlightApplied Sciences

Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant

A modular framework integrating standard RAG, embedding-based retrieval, and LLM-generated structured graphs for e-government question answering.

This paper introduces a hybrid multi-agent graph retrieval-augmented generation (GraphRAG) framework designed to enhance policy-focused question answering in e-government settings. The framework integrates standard RAG, embedding-based retrieval, real-time web search, and LLM-generated structured Graphs to optimize knowledge discovery from public e-government data. This approach aims to provide an overview of a hybrid architecture for operational deployment in e-government settings.

Based on: Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant · Applied Sciences

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KA-RAG: Integrating Knowledge Graphs and Agentic Retrieval-Augmented Generation for an Intelligent Educational Question-Answering Model

A course-oriented question answering framework that integrates a structured Knowledge Graph with an Agentic Retrieval-Augmented Generation workflow.

The paper introduces KA-RAG, a QA framework combining symbolic graph reasoning with dense semantic retrieval. It achieves high retrieval accuracy and semantic consistency on a graduate-level Pattern Recognition course. User surveys show improvements in learning efficiency and satisfaction.

Based on: KA-RAG: Integrating Knowledge Graphs and Agentic Retrieval-Augmented Generation for an Intelligent Educational Question-Answering Model · Applied Sciences

HighlightIEEE Transactions on Artificial Intelligence

TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking

A generative AI solution designed for fact-checking in the health domain using a large language model and graph-based retrieval-augmented generation.

TrumorGPT is a novel AI system that leverages a large language model with few-shot learning to construct semantic health knowledge graphs and perform fact-checking. It addresses hallucination issues common in LLMs by accessing regularly updated semantic health knowledge graphs. Evaluations demonstrate superior performance in fact-checking for public health claims.

Based on: TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking · IEEE Transactions on Artificial Intelligence

HighlightLecture notes in business information processing

Retrieval-Augmented Generation for Entity Alignment in Knowledge Graphs: An Incipient Experiment

A research paper on retrieval-augmented generation for entity alignment in knowledge graphs.

This paper explores the use of retrieval-augmented generation to improve entity alignment in knowledge graphs. The authors propose a method that combines retrieval and generation techniques to enhance the accuracy of entity alignment. The experiment demonstrates the effectiveness of this approach, showing improved results compared to traditional methods.

Based on: Retrieval-Augmented Generation for Entity Alignment in Knowledge Graphs: An Incipient Experiment · Lecture notes in business information processing

HighlightProceedings of the AAAI Conference on Artificial Intelligence

Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs

A paper proposing a multi-objective multi-armed bandit enhanced RAG framework for knowledge graphs.

The authors introduce a framework that adapts to non-stationary environments by selecting the most suitable retrieval method based on user feedback and historical performance. This approach is applied to Retrieval-Augmented Generation (RAG) on knowledge graphs, aiming to enhance reasoning capabilities of large language models. Experiments demonstrate improved performance in both stationary and non-stationary settings.

Based on: Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs · Proceedings of the AAAI Conference on Artificial Intelligence

HighlightarXiv (Cornell University)

Knowledge Prompting: How Knowledge Engineers Use Large Language Models

A study on the use of large language models in knowledge engineering.

The paper explores how knowledge engineers utilize large language models to support semi-automatic tasks. A multimethod study was conducted during a KE hackathon to investigate participants' views, challenges, and skills needed for integrating LLMs into their practices. The findings highlight the potential of prompting as a valuable skill for knowledge engineers working with LLMs.

Based on: Knowledge Prompting: How Knowledge Engineers Use Large Language Models · arXiv (Cornell University)