用知识图谱和大模型提升专利分析效率与深度
KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
- 构建知识图谱+RAG+智能代理三合一框架
- 在真实专利库中实现更高知识提取与连接发现
- 适合科技公司与律所做创新战略与竞争情报
有效管理知识产权是一项重大挑战。传统专利分析依赖耗时的人工检索和僵化的关键词匹配，效率低下且难以揭示大规模专利数据中的复杂关系，影响战略决策。为克服这些局限，我们提出KLIPA框架，利用知识图谱和大语言模型（LLM）显著提升专利分析能力。该框架集成三大组件：结构化知识图谱以映射专利间的显式关系，检索增强生成（RAG）系统挖掘上下文关联，以及智能代理动态确定最优查询解决策略。我们在一个全面的现实世界专利数据库上验证了KLIPA，结果表明其在知识提取、新关系发现及整体运营效率方面均有显著提升。该技术组合提高了检索准确率，降低对领域专家的依赖，并为管理知识产权的企业（如科技公司和法律机构）提供可扩展的自动化解决方案，助力更好地应对战略创新与竞争情报的复杂性。
原文摘要 · Abstract (English)
Effectively managing intellectual property is a significant challenge. Traditional methods for patent analysis depend on labor-intensive manual searches and rigid keyword matching. These approaches are often inefficient and struggle to reveal the complex relationships hidden within large patent datasets, hindering strategic decision-making. To overcome these limitations, we introduce KLIPA, a novel framework that leverages a knowledge graph and a large language model (LLM) to significantly advance patent analysis. Our approach integrates three key components: a structured knowledge graph to map explicit relationships between patents, a retrieval-augmented generation(RAG) system to uncover contextual connections, and an intelligent agent that dynamically determines the optimal strategy for resolving user queries. We validated KLIPA on a comprehensive, real-world patent database, where it demonstrated substantial improvements in knowledge extraction, discovery of novel connections, and overall operational efficiency. This combination of technologies enhances retrieval accuracy, reduces reliance on domain experts, and provides a scalable, automated solution for any organization managing intellectual property, including technology corporations and legal firms, allowing them to better navigate the complexities of strategic innovation and competitive intelligence.
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