Paperlib CCF Rank Scrape Extension

English | 中文

English

Overview

A Paperlib extension that automatically identifies and displays CCF (China Computer Federation) rankings for academic papers. This plugin matches paper publication venues (conferences/journals) against the CCF ranking database and displays the ranking level with color-coded badges in the Paperlib UI.

Features

• Automatic CCF Rank Detection: Automatically identifies CCF rankings (A/B/C) for selected papers
• Intelligent Matching Algorithm: Uses multiple matching strategies including:
  • Exact short name matching (e.g., "SIGCOMM", "CVPR")
  • Bracket-based abbreviation extraction
  • Longest common substring algorithm with coverage rate validation
• Visual Ranking Display: Color-coded badges for different ranking levels:
  • 🔴 CCF-A (Red)
  • 🟠 CCF-B (Orange)
  • 🟢 CCF-C (Green)
  • ⚪ CCF-none (Gray)
• Comprehensive Database: Built-in database with 600+ CCF-ranked conferences and journals

Installation

1. Install via Paperlib Extension Marketplace (recommended)
2. Or manually install:

   npm install
   npm run build

   Then load the dist/ folder as a local extension in Paperlib

Usage

Simply select a paper in Paperlib, and the extension will automatically:

1. Extract the publication venue information
2. Match it against the CCF ranking database
3. Display the ranking badge in the paper details panel

Development

Build the extension

npm run build

Type checking

npm run prebuild

Technical Details

• Language: TypeScript
• Build Tool: Vite
• Output Format: CommonJS
• Paperlib API Version: 0.1.7+

Repository

• Homepage: https://github.com/Duducoco/paperlib-ccf-rank-scrape-extension

中文

概述

一个 Paperlib 扩展插件,用于自动识别和显示学术论文的 CCF(中国计算机学会)等级评定。该插件将论文的发表场所(会议/期刊)与 CCF 等级数据库进行匹配,并在 Paperlib UI 中以彩色标签显示等级信息。

功能特性

• 自动 CCF 等级检测: 自动识别选中论文的 CCF 等级(A/B/C)
• 智能匹配算法: 使用多种匹配策略:
  • 精确短名称匹配(如 "SIGCOMM"、"CVPR")
  • 括号缩写提取
  • 最长公共子串算法配合覆盖率验证
• 可视化等级展示: 不同等级使用不同颜色标签:
  • 🔴 CCF-A (橙红色)
  • 🟠 CCF-B (橙色)
  • 🟢 CCF-C (绿色)
  • ⚪ CCF-none (灰色)
• 完整数据库: 内置 600+ 条 CCF 推荐会议和期刊数据

安装方法

1. 通过 Paperlib 扩展市场安装(推荐)
2. 或手动安装:

   npm install
   npm run build

   然后在 Paperlib 中加载 dist/ 文件夹作为本地扩展

使用方法

只需在 Paperlib 中选择一篇论文,扩展将自动:

1. 提取出版场所信息
2. 与 CCF 等级数据库进行匹配
3. 在论文详情面板中显示等级标签

开发

构建扩展

npm run build

类型检查

npm run prebuild

技术细节

• 开发语言: TypeScript
• 构建工具: Vite
• 输出格式: CommonJS
• Paperlib API 版本: 0.1.7+

代码仓库

• 主页: https://github.com/Duducoco/paperlib-ccf-rank-scrape-extension

许可证

详见 package.json

作者

Duducoco