Knowledge-data-evaluation

A high-efficiency data management tool designed for analyzing entity relationships and comprehensively evaluating data quality.

Overview

This system leverages advanced algorithms to evaluate data quality across multiple dimensions, including quantity, consistency, and association, providing a clear view of data quality to assist users in optimizing datasets.

Installation Guide

1. Ensure Python 3.8 or higher is installed.
2. Install ber-base-chinese https://huggingface.co/google-bert/bert-base-chinese
3. Clone the repository and install dependencies: git clone https://github.com/Learning0411/Knowledge-data-evaluation.git

Configuration

Before running the system, you may need to configure the path to your own bert-base-chinese model in similarity_computation.py. Replace the default path with the path to your local model directory.

Usage

To run the system, you need to prepare your data as triples in a CSV file. Each row in the CSV should represent a triple with three columns: subject, predicate, and object.

Contribution Guidelines

Contributions and pull requests are welcome. Please adhere to the guidelines specified in the CONTRIBUTING.md file.

Maintainers

Principal Developer: Learning0411 gww723 lilinze123 chanjuanzhou wkq8008 same0709 haha123agfd Liusf6416 hankatsufumi yiayg wclftx crimsondde cquptljl 2048kbs