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How do I use KGs for Retrieval-Augmented Generation (RAG)? (2.1 – Prompt Enhancement) – length: up to one page

Lead: DiegoDraft Daniel Burkhardt : 

Contributors:

• Daniel Burkhardt (FSTI)
• Robert David (SWC)
• Diego Collarana (FIT)
• Daniel Baldassare (doctima)
• Michael Wetzel (Coreon)

Explanation of concepts

• Retrieval-augmented generation (RAG) methods combine retrieval mechanisms with generative models to enhance the output of LLMs by incorporating external knowledge. By grounding the generated output in specific and relevant information, RAG methods enhance improve the quality and accuracy of the generated output.

• Types of RAG:

  • Conventional RAG has three components: 1) Knowledge Base, typically created by chunking text documents, transforming them into embeddings, and storing them in a vector store. 2) Retriever searches the vector database for chunks that exhibit high similarity to the query. 3) Generator feeds the retrieved chunks, alongside the original query, to an LLM to generate the final response.
  • Graph RAG integrates knowledge graphs into the RAG framework, allowing for the retrieval of structured data that can provide additional context and factual accuracy to the generative model. 
• RAG is used in various natural language processing tasks, including question-answering, information extraction, sentiment analysis, and summarization. It is particularly beneficial in scenarios requiring domain-specific knowledge.

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We describe various solutions for integrating knowledge graphs into RAG systems to improve accuracy, reliability, and explainability. 

Answer 1:

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Knowledge Graph as a Database with Natural Language Queries (NLQ)

Description: This solution treats the knowledge graph as a structured database and leverages natural language queries (NLQ) to retrieve specific information. The implementation steps are as follows:

• First, the user's question is processed to extract key entities and relationships using entity linking and relationship extraction techniques. (Natural Language Understanding)
• Next, the natural language query is partially or fully mapped into a graph query language, e.g., Cypher or SPARQL. (Graph Query Construction)
• Then, the constructed graph query is executed against the knowledge graph database, which retrieves precise and targeted information from the knowledge graph. (Knowledge Graph Execution)
• Finally, the retrieved results are passed to the LLM for summarization or further processing to generate the final answer. (Response generation)

Considerations:

• Accurate Query Mapping: Requires advanced NLP techniques to accurately map natural language queries to graph queries. Entity linking and relationship extraction must be precise to ensure correct query formulation.
• Performance Efficiency: Executing complex graph queries may impact performance, especially with large-scale knowledge graphs. Optimization of graph databases and queries is necessary for real-time applications.
• Scalability: The system should handle growing knowledge graphs without significant performance loss. Scalable graph database solutions are essential.
• User Experience: The system must effectively interpret user intent from natural language inputs. Providing clear and concise answers enhances usability and trust.

Standards and Protocols:

• Compliance with Data Standards: Ensure the knowledge graph adheres to relevant data modeling standards. Where applicable, utilize standardized vocabularies and ontologies.
• Interoperability: Design the system for various graph databases and query languages. Support integration with external data sources and systems.

Answer 2: KG-Guided Retrieval Mechanisms

Description

Contributors:

• Daniel Burkhardt (FSTI)
• Robert David (SWC)
• Diego Collarana (FIT)
• Daniel Baldassare (doctima)
• Michael Wetzel (Coreon)

Short definition/description of this topic: KG-Guided Retrieval Mechanisms involve using, for example, knowledge graphs or vector databases to enhance the retrieval process in RAG systems. Knowledge graphs provide a structured representation of knowledge, enabling more precise and contextually aware information retrieval of information. This approach can directly query knowledge graphs or use them to augment queries to other data sources, improving the relevance and accuracy of the retrieved information.

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• Initial RAG idea: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• RAG is commonly used with vector databases.
  • can only grasp semantic similarity represented in the document content
  • only unstructured data
  • vector distance instead of a DB search limits the retrieval capabilities
• Graph RAG uses knowledge graphs as part of the RAG system
  • KGs for retrieval (directly), meaning the database is storing KG data
  • KGs for retrieval via a semantic layer, potentially retrieving over different data sources of structured and unstructured data
  • KGs for augmenting the retrieval, meaning the queries to some database is databases are modified via KG data
• Via Graph RAG, we can
  • ingest additional semantic background knowledge (knowledge model) not represented in the data itself
    • additional related knowledge based on defined paths (rule-based inference)
    • focus on certain specific aspects of a data set for the retrieval (search configuration)
    • personalization: represent different roles for retrieval via ingesting role description data into the retrieval (especially important in an enterprise environment)
  • reasoning
  • linked data makes factual knowledge related to the LLM-generated knowledge and thereby provide provides a means to check for correctness
  • explainable AI: provide justifications via KG
  • consolidate different data sources: unstructured, semi-structured, and structured (enterprise knowledge graph scenario)
  • doing the actual retrieval via KG queries: SPARQL
  • hybrid retrieval: combine KG-based retrieval with vector databases or search indexes

Answer

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3: Hybrid Retrieval Combining KGs and Dense Vectors

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Contributors:

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Draft from Daniel Burkhardt: 

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