HighlightCurated by Aramai EditorialarXiv (Cornell University)

SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context

A training-free routing framework for knowledge graph retrieval-augmented generation.

The authors propose a simple and effective routing framework, SkewRoute, which balances performance and cost in knowledge graph retrieval-augmented generation. The framework is designed to direct queries to the most suitable language models based on score skewness of retrieved contexts. It achieves over 3x higher routing effectiveness while reducing runtime compared to existing methods.

Based on: SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context · arXiv (Cornell University)

HighlightCurated by Aramai Editorialopenalex.org

Knowledge Graph-Guided Retrieval Augmented Generation

Paper proposing a method for retrieval augmented generation using knowledge graphs.

This paper presents a method that combines knowledge graph-guided retrieval with augmented generation to improve the performance of language models.,The proposed approach uses a knowledge graph to guide the retrieval process and augment the generated text.,Experimental results demonstrate the effectiveness of the proposed method in improving the accuracy and coherence of generated text.

Based on: Knowledge Graph-Guided Retrieval Augmented Generation

HighlightCurated by Aramai Editorialopenalex.org

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

HighlightCurated by Aramai Editorialopenalex.org

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

HighlightCurated by Aramai EditorialKnowledge-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

HighlightCurated by Aramai EditorialarXiv (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)

HighlightCurated by Aramai EditorialApplied Sciences

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

HighlightCurated by Aramai Editorialopenalex.org

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

HighlightCurated by Aramai Editorialopenalex.org

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

HighlightCurated by Aramai EditorialCommunications 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

HighlightCurated by Aramai Editorialopenalex.org

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

HighlightCurated by Aramai EditorialLecture 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