LLM ONTOLOGY AND RAG基本关系

The ontology acts as the “knowledge source” for the RAG process, which retrieves relevant facts and relationships from the ontology to provide context to the LLM

  • LLM: The large language model is the final engine that generates the response.
  • Ontology: An ontology provides a formal, structured representation of knowledge, defining entities and their relationships in a specific domain. This structured data is more precise than simple text documents.
  • RAG: The RAG process acts as the bridge between the ontology and the LLM.
  • When a user asks a question, the RAG system uses the query to search the ontology for relevant facts and relationships.
  • The retrieved, structured information from the ontology is then passed to the LLM along with the original question.
  • The LLM uses this retrieved context to generate a more accurate, specific, and well-grounded answer, reducing hallucinations.
    Benefits of using ontologies in RAG
  • Improved accuracy: By using a structured ontology, the RAG system can retrieve precise, fact-based context, leading to more accurate LLM responses.
  • Enhanced reasoning: Ontologies enable the LLM to make better-reasoned connections between different pieces of information, which is especially useful for complex queries.
  • Increased transparency: The system can attribute its answers back to the specific facts and relationships in the ontology, making the source of information more verifiable.
  • Domain-specific knowledge: Integrating an ontology allows the LLM to access and use detailed, specific knowledge from a particular domain that it wasn’t originally trained on

The Role of Ontologies with LLMs - Enterprise Knowledge

参考url The Role of Ontologies with LLMs - Enterprise Knowledge

使用本体直接tuning LLM
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使用ON-RAG 为LLM提供输入
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ON-RAG：OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models

参考：
1.论文： OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models
2. 微软实现： GitHub - microsoft/ograg2: OGRAG - Release Version
3. 亚信科技文章: 本体与LLMs的协同赋能研究综述

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