EntityIdentification
Identify whether two sets of data are from the same entity. 识别两组数据是否来自同一主体
This is a MCP (Model Context Protocol) server. 这是一个支持MCP协议的服务器。
Data Comparison Tool
This tool provides a comprehensive way to compare two sets of data, evaluating both exact and semantic equality of their values. It leverages text normalization and a language model to determine if the data originates from the same entity.
Features
- Text Normalization: Converts text to lowercase, removes punctuation, and normalizes whitespace.
- Value Comparison: Compares values directly and semantically (ignoring order for lists).
- JSON Traversal: Iterates through each key in the JSON objects and compares corresponding values.
- Language Model Integration: Uses a generative language model to assess semantic similarity and provide a final judgment on whether the data comes from the same entity.
Installation
To use this tool, ensure you have the necessary dependencies installed. You can install them using pip:
Usage
Functions
- normalize_text(text):
- Normalizes the input text by converting it to lowercase, removing punctuation, and normalizing whitespace.
- compare_values(val1, val2):
- Compares two values both exactly and semantically.
- If the values are lists, it ignores the order of elements for semantic comparison.
- compare_json(json1, json2):
- Compares two JSON objects key by key.
- Uses
compare_values
to evaluate each key's values. - Integrates a language model to assess semantic similarity and provides a final judgment.
Example
Contributing
Contributions are welcome! Please open an issue or submit a pull request.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Contact
If you have any questions or suggestions, please contact me:
- Email: u3588064@connect.hku.hk
- GitHub: u3588064@connect.hku.hk。
Wechat
This server cannot be installed
Сервер MCP, который помогает определить, принадлежат ли два набора данных одной и той же сущности, сравнивая как точное, так и семантическое равенство посредством нормализации текста и интеграции языковой модели.
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