What You Need to Know about Knowledge Graphs and RAG Systems

Large language models, a type of generative AI, have significant transformative potential that can be harnessed for business benefits,  particularly when used in a responsible way.  These models can be made even more powerful when combined with external knowledge bases to produce more informative, accurate, and comprehensive responses through what is known as Retrieval-Augmented Generation (RAG).

At the heart of successful RAG systems are a combination of RAG+knowledge graphs — a structured and semantically rich representation of information. In this blog post, we describe what knowledge graphs are and how they support RAG systems for LLMs.

What are knowledge graphs?

A knowledge graph represents a network of real-world entities (people, places, events, concepts) and the relationships between them. Unlike simple keyword matching, knowledge graphs capture the deeper meaning and context behind the data, therefore taking a semantic approach that can provide richer representations of information.

Knowledge graphs often use what is known as a Resource Description Framework (RDF) to represent their data in the form of subject-predicate-object triples (e.g., “Dostoevsky” — “is author” — “Crime and Punishment”).  This knowledge graph relationship can be described as in the figure below.

Why knowledge graphs matter for RAG

Knowledge graphs offer several significant advantages for RAG systems. Firstly, they provide a structured way for LLMs to understand the relationships between entities, leading to improved contextual understanding. This helps RAG systems correctly interpret queries and generate more relevant, fact-based responses.

Secondly, with access to a knowledge graph, RAG systems can pull facts, statistics, and other relevant information directly into their responses, enriching their outputs. Thirdly, knowledge graphs are useful for handling complex queries.

Finally, knowledge graphs can reduce hallucinations (incorrect or misleading information) that can sometimes generated by large language models by providing a grounding mechanism, making RAG responses more reliable.

We can list two drawbacks in knowledge graph RAG systems: (i) the LLM needs “have” good understanding of Cypher query language if an implementation via Neo4j; (ii) the increase in chain complexity can decrease the robustness of responses.

How to Use Knowledge Graphs with RAG

Knowledge bases can be built using both structured databases (Wikipedia, Wikidata, industry-specific databases) or unstructured text analyzed with natural language processingtechniques like entity recognition, a way of extracting information from text.

Once a knowledge base has been created, it can be stored in a graph database (like Neo4j) for efficient data storage and retrieval. From here, methods can be developed to query the knowledge graph, using languages like Cypher (for Neo4j) or SPARQL, to extract relevant information. Finally, the retrieved knowledge can be integrated into the LLM’s input or as part of the generation process.

Examples of RAG Systems with Knowledge Graphs

Projects like Graph RAG and combinations of Neo4j + LangChain demonstrate how RAG systems can be powered with knowledge graphs. Graph RAG uses a graph database to enhance retrieval accuracy and query understanding, while Neo4j +LangChain allows you to build knowledge-graph-powered question-answering systems.

The Future is Bright

The knowledge graphs represent powerful potential. As they become more accessible and sophisticated, RAG systems will become more prevalent in a wide range of applications. For example, RAG systems that rely on knowledge graphs could be used to create customer support chatbots that truly understand your products or search engines that can effectively handle complex, nuanced queries.  However, with great power comes great responsibility. With such vital applications, it is even more essential to manage the risks of large language models to increase trust and effectiveness and reduce the risk of reputational and financial damage.

Schedule a demo with our experts to find out how Holistic AI can help you shield against generative AI risks.