HighlightCommunications in computer and information science

Knowledge Graph-Based Legal Query System with LLM and Retrieval Augmented Generation

A paper proposing a knowledge graph-based legal query system using large language models and retrieval augmented generation.

The authors propose a knowledge graph-based legal query system that leverages large language models and retrieval augmented generation to improve query efficiency. The system is designed for legal applications, utilizing a knowledge graph to store and retrieve relevant information. This approach aims to enhance the accuracy and speed of legal queries by combining the strengths of both knowledge graphs and large language models.

Based on: Knowledge Graph-Based Legal Query System with LLM and Retrieval Augmented Generation · Communications in computer and information science

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Enhancing Operations at the Columbus Control-Center: A Hybrid Approach Utilizing Large Language Models, Knowledge Graphs, and Retrieval-Augmented Generation

A paper investigating a hybrid approach combining Large Language Models with Knowledge Graphs and Retrieval-Augmented Generation.

The paper proposes a hybrid system to enhance operational efficiency at the Columbus Control-Center, leveraging Large Language Models, Knowledge Graphs, and Retrieval-Augmented Generation. The system aims to automate routine tasks and provide real-time support for flight control teams. It combines the strengths of LLMs, KGs, and RAG to create a more intelligent and responsive support system.

Based on: Enhancing Operations at the Columbus Control-Center: A Hybrid Approach Utilizing Large Language Models, Knowledge Graphs, and Retrieval-Augmented Generation

HighlightLecture notes in computer science

OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models

A paper on using retrieval-augmented generation to improve biomedical code mapping leveraging ontology knowledge graphs and large language models.

The authors propose OntologyRAG, a method for better and faster biomedical code mapping. They leverage ontology knowledge graphs and large language models to improve the accuracy and efficiency of code mapping. The approach uses retrieval-augmented generation to combine the strengths of both knowledge graphs and language models.

Based on: OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models · Lecture notes in computer science

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GRAG: Graph Retrieval-Augmented Generation

A method for graph retrieval-augmented generation that tackles networked documents.

GRAG addresses limitations of naive RAG by retrieving textual subgraphs and integrating joint textual and topological information into LLMs. It proposes a divide-and-conquer strategy for efficient retrieval and incorporates textual graphs into LLMs through two views. Experiments demonstrate GRAG's effectiveness in multi-hop reasoning on textual graphs.

Based on: GRAG: Graph Retrieval-Augmented Generation

HighlightJournal of Engineering Design

Multilingual graph retrieval-augmented generation for product design using design knowledge

A framework that integrates multilingual design knowledge to improve product design using large language models.

The authors propose a framework, MDKG-RAG, which extracts and integrates multilingual design knowledge from various sources. This framework uses large language models to dynamically optimize and extract relevant information for product design. Experiments demonstrate the effectiveness of MDKG-RAG in improving answer similarity and context recall.

Based on: Multilingual graph retrieval-augmented generation for product design using design knowledge · Journal of Engineering Design

HighlightScientific Reports

Construction of intelligent decision support systems through integration of retrieval-augmented generation and knowledge graphs

Proposes a framework for intelligent decision support systems using retrieval-augmented generation and knowledge graphs.

The article presents a novel architecture that combines generative models with structured knowledge representations to improve decision accuracy, transparency, and context relevance. The proposed method is tested on three areas: financial services, healthcare management, and supply chain management. It shows improvement in cross-domain reasoning and ambiguous queries compared to using either technology alone.

Based on: Construction of intelligent decision support systems through integration of retrieval-augmented generation and knowledge graphs · Scientific Reports

HighlightFrontiers in Artificial Intelligence

Advancing engineering research through context-aware and knowledge graph–based retrieval-augmented generation

A study on improving the accuracy of large language models in generating technical content.

The authors propose a new Retrieval-Augmented Generation (RAG) model for engineering domains, which uses contextual information to improve relevance. The model is built on the n8n automation system and can retrieve densely linked concepts from multiple knowledge graphs. This approach aims to mitigate the shortcomings of traditional RAG techniques in treating isolated information.

Based on: Advancing engineering research through context-aware and knowledge graph–based retrieval-augmented generation · Frontiers in Artificial Intelligence

HighlightJournal of the American Medical Informatics Association

Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation

Study on using large language models and a knowledge graph to triage patient messages for emergency care.

The study evaluates the effectiveness of four models in detecting emergency messages in patient portals, with a focus on integrating large language models (LLMs) with a knowledge graph. The results show that the model incorporating a global search within the knowledge graph outperformed other approaches. This research contributes to the development of AI-assisted triage systems for improving patient safety.

Based on: Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation · Journal of the American Medical Informatics Association

HighlightarXiv (Cornell University)

Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching

A proposed model for schema matching using knowledge graphs and retrieval-augmented generation.

The authors propose a Knowledge Graph-based Retrieval-Augmented Generation model (KG-RAG4SM) to address semantic ambiguities in schema matching. The model introduces novel vector-based, graph traversal-based, and query-based graph retrievals. Experimental results show that KG-RAG4SM outperforms state-of-the-art methods in terms of precision and F1 score on various datasets.

Based on: Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching · arXiv (Cornell University)

HighlightProceedings of the AAAI Conference on Artificial Intelligence

Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs

A paper proposing a multi-objective multi-armed bandit enhanced RAG framework for knowledge graphs.

The authors introduce a framework that adapts to non-stationary environments by selecting the most suitable retrieval method based on user feedback and historical performance. This approach is applied to Retrieval-Augmented Generation (RAG) on knowledge graphs, aiming to enhance reasoning capabilities of large language models. Experiments demonstrate improved performance in both stationary and non-stationary settings.

Based on: Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs · Proceedings of the AAAI Conference on Artificial Intelligence

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SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generation

A paper proposing a novel method called SimGRAG for knowledge graph-driven retrieval-augmented generation.

The authors propose SimGRAG, a two-stage process that aligns query texts and KG structures using an LLM to transform queries into a desired graph pattern. They also develop an optimized retrieval algorithm. Experiments show that SimGRAG outperforms state-of-the-art methods in question answering and fact verification.

Based on: SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generation

HighlightarXiv (Cornell University)

Knowledge Prompting: How Knowledge Engineers Use Large Language Models

A study on the use of large language models in knowledge engineering.

The paper explores how knowledge engineers utilize large language models to support semi-automatic tasks. A multimethod study was conducted during a KE hackathon to investigate participants' views, challenges, and skills needed for integrating LLMs into their practices. The findings highlight the potential of prompting as a valuable skill for knowledge engineers working with LLMs.

Based on: Knowledge Prompting: How Knowledge Engineers Use Large Language Models · arXiv (Cornell University)