基于大语言模型与知识图谱的智能论文生成工具开发构想

news2025/3/19 13:31:34

基于大语言模型与知识图谱的智能论文生成工具开发构想

在这里插入图片描述

一、研究背景与意义

1.1 学术写作现状分析

1.2 技术突破方向

二、系统架构设计

2.1 整体架构

2.2 核心模块交互

三、关键技术实现

3.1 基于LLM的内容生成

class PaperGenerator:
    def __init__(self, model_name="gpt-4"):
        self.model = load_llm(model_name)
        self.knowledge_graph = load_kg("academic")
        
    def generate_outline(self, topic):
        # 结合知识图谱生成提纲
        concepts = self.knowledge_graph.get_related_concepts(topic)
        prompt = f"基于以下概念生成论文提纲：{concepts}"
        return self.model.generate(prompt)
    
    def expand_section(self, section_title):
        # 基于文献知识扩展内容
        papers = self.knowledge_graph.get_related_papers(section_title)
        context = "\n".join([p.abstract for p in papers])
        return self.model.generate(f"扩展章节：{section_title}", context=context)

3.2 学术知识图谱构建

3.3 格式规范引擎

{
  "format_rules": {
    "APA": {
      "citation": {
        "pattern": "(作者, 年份)",
        "example": "(Smith et al., 2020)"
      },
      "reference": {
        "order": ["author", "year", "title", "journal"],
        "separator": ". "
      }
    },
    "IEEE": {
      "equation": {
        "numbering": "section-based",
        "format": "(1)"
      }
    }
  }
}

四、创新特色

4.1 多模态写作支持

4.2 智能协作机制

class CollaborationEngine:
    def __init__(self):
        self.operations = []
        
    def apply_operation(self, op):
        # 使用OT算法处理并发编辑
        transformed = self._transform(op)
        self.operations.append(transformed)
        return transformed
    
    def _transform(self, op):
        # 操作转换逻辑
        for existing_op in self.operations:
            op = self._resolve_conflict(op, existing_op)
        return op