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FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs

A novel flexible modular framework for retrieval-augmented generation using knowledge graphs.

The paper proposes a flexible modular framework, FRAG, which improves retrieval quality while maintaining flexibility in knowledge graph-based retrieval-augmented generation. FRAG estimates the hop range of reasoning paths and applies tailored pipelines to ensure efficient and accurate reasoning path retrieval. The method does not require extra LLM fine-tuning or calls, significantly boosting efficiency and conserving resources.

Based on: FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs

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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Automating construction contract review using knowledge graph-enhanced large language models

A paper that explores the use of knowledge graphs and large language models to automate construction contract review.

The authors propose a method for automating construction contract review using knowledge graphs and large language models. They enhance a pre-trained LLM with a knowledge graph, which is used to extract relevant information from contracts. The enhanced model is then evaluated on its ability to accurately identify issues in construction contracts.

Based on: Automating construction contract review using knowledge graph-enhanced large language models · Automation in Construction

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DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation

Proposes a hybrid QA framework integrating knowledge graph construction and semantic vector retrieval.

DO-RAG is a scalable and customizable QA framework that integrates multi-level knowledge graph construction with semantic vector retrieval. It employs an agentic chain-of-thought architecture to extract structured relationships from unstructured documents, constructing dynamic knowledge graphs. The system fuses graph and vector retrieval results to generate context-aware responses.

Based on: DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation

HighlightKnowledge-Based Systems

Pythia-RAG: Retrieval-augmented generation over a unified multimodal knowledge graph for enhanced QA

A paper proposing Pythia-RAG, a retrieval-augmented generation model for question answering.

The authors introduce Pythia-RAG, a unified multimodal knowledge graph that combines retrieval and generation capabilities. This approach aims to enhance question-answering performance by leveraging the strengths of both methods. The paper presents experiments demonstrating the effectiveness of Pythia-RAG in various QA tasks.

Based on: Pythia-RAG: Retrieval-augmented generation over a unified multimodal knowledge graph for enhanced QA · Knowledge-Based Systems

HighlightarXiv (Cornell University)

TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation

A framework for efficient global reasoning and scalable graph maintenance in knowledge graphs.

The authors propose a tag-guided hierarchical knowledge graph retrieval-augmented generation (RAG) framework, called TagRAG. It introduces two key components: Tag Knowledge Graph Construction and Tag-Guided Retrieval-Augmented Generation. This design aims to improve efficiency and adaptability in global reasoning and graph maintenance.

Based on: TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation · arXiv (Cornell University)

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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

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Integrating Knowledge Graphs with Retrieval-Augmented Generation to Automate IoT Device Security Compliance

A study on integrating knowledge graphs and retrieval-augmented generation for automating IoT device security compliance.

The authors built a knowledge graph to represent NISTIR 8259A standards and integrated it with Retrieval-Augmented Generation (RAG) techniques. They evaluated the performance of RAG using multiple large language models, demonstrating improved query precision and contextual relevance compared to unstructured vector-based retrieval methods.

Based on: Integrating Knowledge Graphs with Retrieval-Augmented Generation to Automate IoT Device Security Compliance

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Natural Language Interface for Goal-Oriented Knowledge Graphs Using Retrieval-Augmented Generation

A paper proposing a natural language interface for goal-oriented knowledge graphs using retrieval-augmented generation.

The authors present a method to enable users to interact with knowledge graphs through natural language queries. They use a retrieval-augmented generation approach, which combines the strengths of both retrieval-based and generation-based methods. This allows for more accurate and efficient querying of knowledge graphs.

Based on: Natural Language Interface for Goal-Oriented Knowledge Graphs Using Retrieval-Augmented Generation

HighlightCommunications in computer and information science

UKRAG: A Unified Knowledge Graph to Enhance Retrieval Augmented Generation Performance

A unified knowledge graph for enhancing retrieval augmented generation performance.

The paper introduces UKRAG, a unified knowledge graph designed to improve the performance of retrieval augmented generation. It aims to provide a comprehensive and structured representation of knowledge to enhance the capabilities of AI models. The proposed approach combines multiple knowledge sources into a single graph structure, enabling more effective information retrieval and generation.

Based on: UKRAG: A Unified Knowledge Graph to Enhance Retrieval Augmented Generation Performance · Communications in computer and information science

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Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation

A paper proposing a framework that combines knowledge graphs and retrieval-augmented generation to enhance large language models in the telecommunications

The authors present a novel framework combining knowledge graph and retrieval-augmented generation techniques to improve large language model performance in telecommunications. The framework leverages a knowledge graph to capture structured information about network protocols, standards, and entities. Results demonstrate the effectiveness of the KG-RAG framework in addressing complex technical queries with precision.

Based on: Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation

HighlightLecture notes in computer science

EcoRAG: A Multi-hop Economic QA Benchmark for Retrieval Augmented Generation Using Knowledge Graphs

A multi-hop economic question answering benchmark for retrieval augmented generation using knowledge graphs.

EcoRAG is a benchmark designed to evaluate the performance of retrieval augmented generation models on multi-hop economic question answering tasks. It uses knowledge graphs and aims to improve the accuracy and efficiency of these models. The benchmark provides a comprehensive evaluation framework for researchers and developers working in this area.

Based on: EcoRAG: A Multi-hop Economic QA Benchmark for Retrieval Augmented Generation Using Knowledge Graphs · Lecture notes in computer science