LLM Knowledge Graph Generation for Context-Rich Data Extraction
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Solution Overview
Problem
Existing database storage systems become inefficient and ineffective when companies attempt to add additional context to their data, as generating and maintaining knowledge graphs manually is time-consuming and resource-intensive, making them undesirable for larger data sets.
Innovation Solution
A knowledge graph generation system (KGS) leveraging a large language model (LLM) to automatically generate knowledge graphs from user inputs, using prompts to guide the LLM in extracting and organizing data into structured formats, including classes, schema, and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual generation and maintenance of knowledge graphs is used, then data context and relationships can be added, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of knowledge graph generation with an automated system using large language models. The LLM processes natural language queries and automatically generates knowledge graphs without human intervention, substituting the manual mechanical effort with an automated AI-based system that achieves the same information extraction and organization functions.
Solution Approach 2:
The system enables self-service knowledge graph generation where users can directly query information needs in natural language and the system automatically generates the appropriate knowledge graphs. This eliminates the need for users to manually create or maintain knowledge graphs, allowing the system to serve itself by automatically processing and organizing information based on user queries.
2Loss of information
If manual generation and maintenance of knowledge graphs is used, then data context and relationships can be added, but resource requirements increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of knowledge graph generation with an automated system using large language models. The LLM processes natural language queries and automatically generates knowledge graphs without human intervention, substituting the manual mechanical effort with an automated AI-based system that achieves the same information extraction and organization functions.
Solution Approach 2:
The patent introduces a large language model as an intermediary between the user's information needs and the knowledge graph generation process. This intermediary component automatically translates natural language queries into structured knowledge graph representations, reducing the direct resource burden on the system by using the LLM's computational capabilities to handle the complex generation tasks.
3Ease of manufacture
If database storage is used, then cost effectiveness is achieved, but additional context cannot be effectively added
Solution Approach 1:
The patent creates a multi-functional system that combines database storage capabilities with knowledge graph generation capabilities. The system can both store data cost-effectively in databases and generate contextual knowledge graphs when needed, allowing the same system to serve multiple functions: efficient storage and enhanced contextual analysis, thereby achieving both cost effectiveness and adaptability.
Data Source
AI summary
System, method, and various embodiments for a knowledge graph generation system are described herein. An embodiment operates by receiving a command to generate a knowledge graph, and identifying a large language model (LLM) configured to parse documents in accordance with a prompt. A prompt for the LLM is generated, and a table, as requested via the prompt, is returned. The knowledge graph is generated based on the table, the knowledge graph including data extracted from the one or more documents by the large language model organized in accordance with the knowledge graph. The generated knowledge graph is returned.