HighlightCurated by Aramai Editorialopenalex.org

Scalable Extraction and Adoption of Shapes for Improving Data Quality and Query Processing in Knowledge Graphs

A thesis proposing techniques to improve data quality, efficient data access, and interoperability in Knowledge Graphs.

The resource proposes Quality Shapes Extraction (QSE) and SHACTOR to enhance data quality in Knowledge Graphs. It also introduces 'shapes statistics' for optimizing SPARQL query processing over KGs. The approach is demonstrated on both synthetic and real-world datasets, showing potential improvements in query performance.

Based on: Scalable Extraction and Adoption of Shapes for Improving Data Quality and Query Processing in Knowledge Graphs

HighlightCurated by Aramai Editorialopenalex.org

Enhancing Vector based Retrieval Augmented Generation with Contextual Knowledge Graph Construction

A novel approach to enhancing vector-based RAG models using contextual knowledge graph construction.

The authors introduce Contextual Knowledge Graph Construction (CKGC), a method that dynamically builds a knowledge graph to enhance information retrieval and question answering tasks. CKGC leverages text chunking, large language models, and ontology mapping to construct a contextualized knowledge graph. Experiments demonstrate significant improvements in Mean Reciprocal Rank and Top-k Accuracy.

Based on: Enhancing Vector based Retrieval Augmented Generation with Contextual Knowledge Graph Construction

HighlightCurated by Aramai EditorialProceedings of the AAAI Symposium Series

StructuGraphRAG: Structured Document-Informed Knowledge Graphs for Retrieval-Augmented Generation

A method for constructing knowledge graphs to enhance retrieval-augmented generation.

This paper presents StructuGraphRAG, a method that leverages document structures to inform the extraction process and constructs knowledge graphs. The approach is designed to enhance retrieval-augmented generation (RAG) for social science research. Experimental results show improved accuracy, comprehensiveness, and contextual relevance compared to traditional RAG methods.

Based on: StructuGraphRAG: Structured Document-Informed Knowledge Graphs for Retrieval-Augmented Generation · Proceedings of the AAAI Symposium Series

HighlightCurated by Aramai EditorialPeerJ Computer Science

Evaluating FAIR Digital Object and Linked Data as distributed object systems

A paper evaluating the FAIR Digital Object concept and its implementations.

The authors evaluate FAIR Digital Object (FDO) as a global distributed object system using five conceptual frameworks. They compare FDO with established Linked Data practices and Web architecture, providing recommendations for both communities. The paper discusses the history of the Semantic Web and its relevance to FDO adoption.

Based on: Evaluating FAIR Digital Object and Linked Data as distributed object systems · PeerJ Computer Science

HighlightCurated by Aramai Editorialopenalex.org

Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering

A novel customer service question-answering method that amalgamates RAG with a knowledge graph.

The paper introduces a method that constructs a knowledge graph from historical issues to improve retrieval accuracy and answering quality. It combines retrieval-augmented generation (RAG) with a knowledge graph, preserving intra-issue structure and inter-issue relations. Empirical assessments show improved performance over baseline methods in key metrics.

Based on: Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering

HighlightCurated by Aramai EditorialInformation

Construction of Knowledge Graphs: Current State and Challenges

A research paper on the current state and challenges of constructing knowledge graphs.

The authors discuss the main graph models for knowledge graphs, introduce requirements for future construction pipelines, and evaluate the state-of-the-art. They identify areas in need of further research and improvement. The paper provides an overview of necessary steps to build high-quality knowledge graphs, including metadata management and quality assurance.

Based on: Construction of Knowledge Graphs: Current State and Challenges · Information

HighlightCurated by Aramai Editorialopenalex.org

Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization

A paper introducing SMART-SLIC, a domain-specific LLM framework integrating RAG with KG and vector store.

The authors present SMART-SLIC, a framework that combines retrieval-augmented generation (RAG) with knowledge graphs (KG) and vector stores to improve question answering accuracy in specific domains. The framework is designed to be generalizable and adaptable to various specialized domains. It aims to mitigate hallucinations, reduce fine-tuning needs, and attribute information sources.

Based on: Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization

HighlightCurated by Aramai EditorialLecture notes in computer science

Enhancing Retrieval-Augmented Generation Models with Knowledge Graphs: Innovative Practices Through a Dual-Pathway Approach

A research paper proposing a dual-pathway approach to enhance retrieval-augmented generation models using knowledge graphs.

The authors present a novel method for improving retrieval-augmented generation models by incorporating knowledge graphs. This approach involves a dual-pathway framework that combines the strengths of both retrieval and generation components. The proposed method is evaluated on several benchmarks, demonstrating its effectiveness in enhancing model performance.

Based on: Enhancing Retrieval-Augmented Generation Models with Knowledge Graphs: Innovative Practices Through a Dual-Pathway Approach · Lecture notes in computer science

HighlightCurated by Aramai Editorialopenalex.org

Knowledge Graph Reasoning and Security Assurance Decision-Making Based on Online Retrieval Augment Generation

A paper proposing a framework for enhancing security assurance using Knowledge Graph reasoning and online Retrieval Augmented Generation.

The authors present a novel approach to risk assessment and mitigation in critical infrastructure, leveraging Knowledge Graphs and large language models. The framework integrates a dynamically updated Knowledge Graph with LLMs to facilitate real-time risk evaluation and proactive strategies. Simulated experiments demonstrate the efficacy of this framework in improving risk identification and response.

Based on: Knowledge Graph Reasoning and Security Assurance Decision-Making Based on Online Retrieval Augment Generation

HighlightCurated by Aramai EditorialarXiv (Cornell University)

From human experts to machines: An LLM supported approach to ontology and knowledge graph construction

Paper exploring the semi-automatic construction of Knowledge Graphs using Large Language Models.

The authors propose a pipeline for constructing Knowledge Graphs with minimal human involvement, leveraging open-source Large Language Models. They demonstrate their method on a deep learning methodology dataset. The paper evaluates the generated content and suggests that LLMs can reduce human effort in KG construction.

Based on: From human experts to machines: An LLM supported approach to ontology and knowledge graph construction · arXiv (Cornell University)

HighlightCurated by Aramai EditorialProceedings of the AAAI Conference on Artificial Intelligence

Knowledge Graph Prompting for Multi-Document Question Answering

A method for formulating context in prompting large language models for multi-document question answering.

The authors propose a Knowledge Graph Prompting (KGP) method to improve multi-document question answering. KGP consists of graph construction and traversal modules, which create a knowledge graph over multiple documents and navigate across nodes to gather supporting passages. The method aims to enhance prompt design and retrieval augmented generation for large language models.

Based on: Knowledge Graph Prompting for Multi-Document Question Answering · Proceedings of the AAAI Conference on Artificial Intelligence

HighlightCurated by Aramai Editorialopenalex.org

HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction

A novel approach to enhance question-answer systems for information extraction from financial documents.

The paper introduces HybridRAG, a combination of Knowledge Graph-based RAG techniques and VectorRAG techniques. It aims to improve information extraction from financial documents by retrieving context from both vector databases and knowledge graphs. Experiments show that HybridRAG outperforms traditional VectorRAG and GraphRAG in terms of retrieval accuracy and answer generation.

Based on: HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction