Automated Factor Graph Database Expansion via LLM Classification
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Solution Overview
Problem
Existing graph databases require manual expansion by engineers when new information is received, making the process inefficient and prone to errors.
Innovation Solution
A novel method for automatically expanding factor graph databases by using a Large Language Model (LLM) to determine whether new information should be treated as an element of an existing entity, a new entity, or a mediator between entities, and adjusting the graph structure accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expansion by engineers is used, then the graph database can be expanded with accurate decisions about entity relationships, but the process becomes inefficient and time-consuming
Solution Approach 1:
The system performs self-service by automatically analyzing novel information and determining entity relationships without requiring engineer intervention. The automated system classifies whether new information represents a new entity, an existing entity, or a relationship between entities, thereby resolving the technical contradiction by eliminating manual time investment while maintaining decision accuracy through algorithmic analysis
2Reliability
If manual expansion by engineers is used, then complex entity relationships can be correctly identified, but the process is prone to human errors
Solution Approach 1:
The patent replaces the mechanical human decision-making process with an automated computational system that analyzes novel information and determines entity relationships. This substitution eliminates human errors while maintaining or improving classification accuracy through consistent algorithmic application, thereby resolving the contradiction between reliability and productivity
3Adaptability or versatility
If the graph structure is expanded to include new entities and relationships, then the database becomes more comprehensive, but the complexity of the graph structure increases
Solution Approach 1:
The system applies segmentation by breaking down the complex task of graph expansion into distinct classification categories: determining whether new information represents a new entity, an existing entity, or a relationship between entities. This structured approach manages graph structure complexity by organizing expansion decisions into manageable segments while maintaining database comprehensiveness
Data Source
AI summary
The expansion of a computer graph database upon receipt of novel information involves identifying whether the novel information should be stored as part of the existing categories contained in the computer graph database, thereby requiring the addition of a new variable to an extant category of the computer graph database, or whether the new information should be stored as a new category that intersect with extant categories of the computer graph database, thereby requiring an extension of a relation between two or more extant categories, or whether the new information should be stored as a new category that only connects to a single extant category, thereby requiring the creation of a new portion of the computer graph database.