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Fusion-Based Retrieval-Augmented Generation for Complex Question Answering with LLMs

A paper proposing a Retrieval-Augmented Generation model that integrates structured and unstructured knowledge.

The paper presents a dual-channel knowledge retrieval mechanism that targets structured and unstructured sources. A unified knowledge fusion network integrates both types of information into a coherent generation context, enhancing the accuracy and linguistic quality of generated outputs. The method shows strong stability and generalization in cross-domain tasks.

Based on: Fusion-Based Retrieval-Augmented Generation for Complex Question Answering with LLMs

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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

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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

HighlightAutomation in Construction

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

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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

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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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Translating and Formalizing the MIRAGE Guidelines to a Prototype MIRAGE Ontology and DCAT3 Extension Vocabulary for Glycomics Data Management

A paper on formalizing MIRAGE guidelines into a prototype ontology and DCAT3 extension vocabulary for glycomics data management.

The authors present a comprehensive semantic formalization of MIRAGE guidelines using an integrated RDF ontology framework. The framework models glycan structures, biological specimens, analytical instruments, and experimental processes with formal OWL semantics and SHACL validation constraints. It enables automated quality assessment, federated data querying, and enhanced reproducibility in glycomics research.

Based on: Translating and Formalizing the MIRAGE Guidelines to a Prototype MIRAGE Ontology and DCAT3 Extension Vocabulary for Glycomics Data Management

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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

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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