{"repo":"machuangtao/KG-RAG4SM","free":true,"listed":false,"github":"https://github.com/machuangtao/KG-RAG4SM","clone":"git clone https://github.com/machuangtao/KG-RAG4SM.git","description":"Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching","language":"Python","stars":18,"topics":["graphrag","knowledge-graph","schema-matching","retrieval-augmented-generation","data-integration"],"license":null,"category":"data-pipelines","readme_excerpt":"KG-RAG4SM: Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching This repository provides the source code & data of our paper \"Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching\". Introduction KG-RAG4SM is a knowledge graph-based retrieval-augmented generation (graph RAG) model for schema matching and data integration. - It introduces novel vector-based, graph traversal-based, and query-based graph retrievals, as well as a hybrid approach and ranking schemes that identify the most relevant subgraphs from external large knowledge graphs (KGs). - It leverages the retrieved subgraphs to augment the LLMs and prompts for generating the final results for the complex schema-matching task. - It supports the mainstream LLMs, such as GPT, Mistral, Llama, Gemma, Jellyfish, etc. Quick Start 1. Environment and Dependencies Run the following commands to create a conda environment: Activate the created conda environment and install the dependencies: 2. Clone the project and configure the LLM Clone the project and download the data Configure the GPT models with the OPENAI API KEY Login with huggingface token for Jellyfish and Mistral models, please make sure you have been granted access rights to the model from the huggingface. 3. Run with the preprocessed data to reproduce You can run the code with the preprocessed data (stored in datasets/reproduce/ ) with the generated schema matching questions and retrieved subgraphs from wikdiata. Make sure you h","default_branch":null,"files":null,"tree":[],"storefront":"/r/machuangtao","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/machuangtao/KG-RAG4SM/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}