HighlightCurated by Aramai EditorialJournal of Manufacturing Systems

A Multi-Agent and synergistic Knowledge Graph retrieval-augmented generation framework for intelligent maintenance

This paper proposes a framework for intelligent maintenance using multi-agent and synergistic knowledge graph retrieval-augmented generation.

The authors present a framework that combines multi-agent systems with knowledge graph retrieval-augmented generation to improve intelligent maintenance. The framework is designed to enhance the efficiency and effectiveness of maintenance tasks by leveraging the strengths of both approaches. This work contributes to the development of more advanced maintenance systems that can adapt to complex environments.

Based on: A Multi-Agent and synergistic Knowledge Graph retrieval-augmented generation framework for intelligent maintenance · Journal of Manufacturing Systems

HighlightCurated by Aramai EditorialProceedings of the AAAI Conference on Artificial Intelligence

TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs

A framework for resolving factual conflicts between LLMs' internal knowledge and external information using knowledge graphs.

This paper proposes TruthfulRAG, a framework that leverages knowledge graphs to resolve factual conflicts in RAG systems. It constructs KGs from retrieved content, identifies relevant knowledge through query-based graph retrieval, and employs entropy-based filtering mechanisms to mitigate inconsistencies. The authors claim that TruthfulRAG outperforms existing methods in resolving knowledge conflicts and improving the robustness of RAG systems.

Based on: TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs · Proceedings of the AAAI Conference on Artificial Intelligence

HighlightCurated by Aramai EditorialarXiv

Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models

A paper proposing a framework for automating cause-and-effect logic generation using knowledge graphs and large language models.

The authors present a semantic-AI framework that combines a knowledge graph with a constrained large language model to automate the generation of cause-and-effect logic. The framework builds on an established modular alignment ontology and demonstrates its application on a modular process plant. This approach aims to reduce manual effort in creating engineering specifications.

Based on: Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models · arXiv

HighlightCurated by Aramai EditorialarXiv

Travel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge Graphs

A paper proposing a modular pipeline for building a travel-domain reasoning LLM grounded in an expert-designed knowledge graph.

The authors propose a modular pipeline to build a travel-domain reasoning large language model (LLM) using a domain-specific knowledge graph. The pipeline integrates a travel KG, bottom-up construction procedure, and supervised fine-tuning stage to embed domain knowledge into the LLM. The approach achieves high accuracy on a benchmark dataset.

Based on: Travel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge Graphs · arXiv

HighlightCurated by Aramai EditorialarXiv

XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation

A framework that generates causally grounded explanations for GraphRAG systems.

The paper introduces XGRAG, a graph-native framework for explaining knowledge graph-based retrieval-augmented generation. It employs graph-based perturbation strategies to quantify the contribution of individual graph components on the model answer. The authors conduct experiments comparing XGRAG against an existing explainability baseline and evaluate its robustness across various question types and LLMs.

Based on: XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation · arXiv

HighlightCurated by Aramai EditorialarXiv

CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph

A framework for elevating web-scale corpus construction to structured knowledge organization.

The paper presents Cortex, a three-layer heterogeneous structure (Ontological Corpus Graph) for organizing high-quality corpora. It refines content, evolves ontologies, and enables cross-domain alignment. Comprehensive experiments validate its effectiveness in quality refinement, domain organization, and data synthesis.

Based on: CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph · arXiv

HighlightCurated by Aramai EditorialData & Knowledge Engineering

Semantic constraint validation in knowledge representation for the semantic web: A survey, taxonomy and research challenges

A survey, taxonomy and research challenges on semantic constraint validation.

This paper presents a survey and taxonomy of semantic constraint validation techniques for knowledge representation on the Semantic Web. It identifies research challenges and gaps in current approaches. The authors aim to provide a comprehensive overview of existing methods and their limitations.

Based on: Semantic constraint validation in knowledge representation for the semantic web: A survey, taxonomy and research challenges · Data & Knowledge Engineering

HighlightCurated by Aramai EditorialComputer Science Review

From vectors to knowledge graphs: A comprehensive analysis of modern retrieval-augmented generation architectures

A study on modern retrieval-augmented generation architectures.

The paper analyzes the evolution of retrieval-augmented generation (RAG) models from vector-based representations to knowledge graph-based ones. It provides a comprehensive overview of the current state-of-the-art in RAG architectures and their applications. The authors discuss the benefits and limitations of using knowledge graphs in RAG models.

Based on: From vectors to knowledge graphs: A comprehensive analysis of modern retrieval-augmented generation architectures · Computer Science Review

HighlightCurated by Aramai EditorialarXiv

Peak-Then-Collapse and the Four Interface Channels of Knowledge-Graph Tool Use

A study on the performance of a knowledge-graph tool use recipe with large language models.

The authors test a standard recipe for using knowledge graphs with large language models, observing a 'peak-then-collapse' pattern in performance. They identify four recurring failure modes and argue that interface feedback is a key difference from other tools. The study also explores the effect of self-distillation as a mitigation strategy.

Based on: Peak-Then-Collapse and the Four Interface Channels of Knowledge-Graph Tool Use · arXiv

HighlightCurated by Aramai EditorialarXiv

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

HighlightCurated by Aramai Editorialopenalex.org

When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generation

A framework that calibrates knowledge graph retrieval-augmented generation models.

The paper proposes Ca2KG, a causality-aware calibration framework for KG-RAG. It integrates counterfactual prompting and a panel-based re-scoring mechanism to improve calibration while maintaining predictive accuracy. Experiments on two QA datasets demonstrate the effectiveness of Ca2KG.

Based on: When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generation

HighlightCurated by Aramai EditorialarXiv

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