Knowledge Management Systems (KMS) have long been the backbone for organizing information within organizations. While large language models (LLMs) aid in natural language-based information retrieval from KMS, they may lack specific organizational data. Retrieval-augmented generation (RAG) bridges this gap by retrieving contextually relevant information from KMS using vector databases that store data as mathematical vectors, capturing word meanings and relationships within documents. It feeds this information to the LLM, empowering it to generate more accurate and informative responses.

The RAG technique demands substantial data and computational resources for training and generating models, particularly when dealing with multilingual and intricate tasks. RAG may encounter uncertainty when dealing with structured and unstructured data, impacting the quality of generated content, especially for complex queries. However, relying solely on vector retrieval techniques, while effective for quick retrieval, may limit showing relationships between data points.

Limitations of Vector Retrieval in Capturing Meaning

Vector retrieval chops data into small chunks for embedding, potentially resulting in loss of context and relationships. It often relies on K-Nearest Neighbors (KNN) algorithms for similarity comparisons of data points with their nearest neighbors. KNN struggles with large, complex enterprise datasets and becomes underperforming and time-consuming. This vast dataset impacts its memory and processing power and data noise can impact the algorithm’s decision-making power.

Relying on pre-trained LLMs, vector retrieval systems often lack transparency, raising concerns about bias and complicating troubleshooting efforts. Balancing how well and fast they work, these systems might sacrifice accuracy for speed, and there’s a risk of privacy issues when using them with sensitive data.

Knowledge graphs can be a solution to address these limitations by capturing the meaning and connections between data points, providing a deeper understanding of information.

For example, imagine you’re planning a trip to Italy and want to learn about famous landmarks. A vector retrieval system might return generic information on the Colosseum or Leaning Tower of Pisa. However, with a graph RAG-powered search, by searching for “places to visit near the Leaning Tower of Pisa,” the system would not only provide information about the landmark itself but also connect it to nearby museums, historical sites, and even cafes – all through the power of understanding relationships within the data.

What is a Knowledge Graph

Knowledge graphs or semantic networks organize and integrate information from multiple sources using a graph-based model. A knowledge graph consists of nodes, edges and labels; nodes represent entities or objects, such as people, places, or concepts; edges denote the relationships or connections between these entities, indicating how they are related; and labels offer descriptive attributes for both nodes and edges, aiding in defining their characteristics within the graph structure.

Knowledge graphs store and organize information like mind maps in a Subject-Predicate-Object (SPO) format for connecting information and revealing relationships between entities. The subject comes first, then the predicate (relationship), and then the object in knowledge graphs. For example, in the following sentence, “Eiffel Tower is located in Paris”, ‘Eiffel Tower’ is the subject, ‘is located in’ is the predicate and ‘Paris’ is the object. This interconnected structure of knowledge graphs allows for the efficient handling of complex queries by providing a deep contextual understanding through relationships.

This image is an example of a knowledge graph showcasing a company’s supply chain. It visually represents entities like vendors, warehouses, and products. Arrows connecting these entities illustrate the flow of goods, with vendors supplying warehouses. Ultimately, the graph depicts the journey of products from suppliers to the final customer.

Querying a graph database involves navigating the graph structure to find nodes and relationships based on specific criteria. For instance, in the supply chain knowledge graph, querying it to find bottlenecks could start at the “customer” node and follow “shipped from” edges to warehouses. Analyzing the number of incoming shipments at each warehouse reveals potential congestion points, allowing for better inventory allocation. Subsequently, a query is formulated using a graph query language to traverse the graph and reveal valuable information for better supply chain decision-making.

Advantages of Knowledge Graphs in a RAG System

Knowledge graphs can address the limitations of vector retrieval in multiple ways:

Enhanced Text Analysis: Knowledge graphs facilitate precise interpretation of texts meaning and sentiment analysis by improving understanding of relationships between concepts or entities.

For example, Microsoft Research has introduced GraphRAG to enhance the capabilities of Language Model-based tools. It shows the practical application of GraphRAG in analyzing the Violent Incident Information from News Articles (VIINA) dataset, containing news articles from Russian and Ukrainian sources. When queried about “Novorossiya,” GraphRAG excelled over baseline RAG, accurately retrieving relevant information about the political movement, including its historical context and activities. Its grounding in the knowledge graph ensured superior answers with evidence, enhancing accuracy. Additionally, GraphRAG effectively summarized the dataset’s top themes demonstrating its value in complex data analysis and decision-making.

As organizations increasingly adopt advanced technologies to manage their vast knowledge bases, solutions like AI Fortune Cookie harness the power of knowledge graphs to drive smarter decision-making and innovation. AI Fortune Cookie leverages the power of knowledge graphs to transform enterprise knowledge management by consolidating isolated data sources into interconnected, scalable networks. This secure knowledge management model allows organizations to visualize complex relationships within their data, enabling a deeper understanding and more accurate query responses through custom LLMs and retrieval-augmented generation (RAG). By structuring information into semantic layers, AI Fortune Cookie ensures that natural language queries are handled efficiently across both internal and external data sources for efficient enterprise data management. This approach not only reduces the risk of hallucinations but also enhances decision-making by providing real-time insights grounded in contextually relevant knowledge graphs. The platform’s ability to deliver precise, interconnected information empowers enterprises to streamline enterprise data integration, drive innovation, and safeguard sensitive data.

Diverse Data Integration: Knowledge graphs integrate diverse data types, such as structured and unstructured data, providing a unified perspective that enhances RAG responses.

AI is utilized in pharma sector to accelerate drug discovery. A unique knowledge graph used in the system integrates vast medical data, including structured information like clinical trial data (patient details, drug responses), molecular structures of drugs and diseases, and genomic data, alongside unstructured data like research papers, medical patents, and electronic health records. This integration provides a comprehensive understanding of human diseases, potential drug targets, and drug interactions within biological systems.

Prevention of Hallucination: The well-defined structure, with clear connections between entities of knowledge graphs, helps LLMs avoid generating hallucinations or inaccurate information.

A conversational agent designed to interact with users and provide personalized recommendations and information related to the food industry, uses a knowledge graph to enhance response quality. The knowledge graph in the chatbot plays a vital role in reducing hallucination by providing explicit instructions to the LLM on data interpretation and utilization. By grounding responses in the knowledge graph’s information, the chatbot ensures contextually appropriate and accurate answers, minimizing hallucinations. The knowledge graph also enables prompt engineering, where adjustments are made to the phrasing and information provided to the LLM to control the response tone and level of information conveyed.

Complex Query Handling: Knowledge graphs handle a wide range of complex queries beyond simple similarity measurements, enabling operations like identifying entities with specific properties or finding common categories among them. This enhances the LLM’s ability to generate diverse and engaging text.

A new framework was proposed for handling complex queries on incomplete knowledge graphs. By representing logical operations in a simplified space, the method allows for efficient predictions about subgraph relationships. The framework was used on a network of drug-gene-disease interactions to predict new connections and it was successful in identifying drugs for diseases linked to a certain protein. This involves reasoning about multiple relationships and entities in the network, showcasing the ability of the framework to handle complex queries in a biomedical context.

Reduces Cost: Knowledge Graphs reduce implementation costs for RAG by eliminating the need for multiple components and scaling vector databases, offering significant cost savings and an appealing ROI for organizations.

A knowledge graph was developed to reduce the cost of implementation of LLM by providing contextual information to the language model without the need for extensive retraining or customization. This eliminates the need for costly fine-tuning processes and ensures that the model can access relevant data in real time. Using this RAG can significantly reduce LLM implementation and maintenance expenses, leading to a remarkable 70% cost reduction that translates to an impressive ROI increase of threefold or more.

In conclusion, knowledge graphs play a pivotal role in enhancing RAG systems. By using structured representations of knowledge, they enable more accurate and contextually grounded responses, improving the performance of RAG systems. Their ability to organize and integrate information from diverse sources empowers RAG systems to tackle complex queries, facilitate better decision-making, and provide users with trustworthy answers.

Explore Random Walk’s resources on Large Language Models, Knowledge Management Systems, RAG, and Knowledge Graphs. Discover how to build smarter systems and transform your knowledge management strategies.