Dynamic In-Memory Knowledge Graph Construction
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Solution Overview
Problem
Existing knowledge graph management systems face challenges in dynamic adaptation and efficient data processing due to inflexible storage and complex maintenance requirements, leading to high costs and time-consuming reorganization tasks when reusing facts across different contexts.
Innovation Solution
A dynamic in-memory construction method for knowledge graphs that separates node information from the graph structure, allowing for on-demand creation and modification of knowledge graphs using a processor-accessible memory system, where node identifiers are managed independently to facilitate fast and flexible graph construction without altering underlying content records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If knowledge graphs are constructed using traditional storage methods with fixed schemas, then data consistency is maintained, but system flexibility and adaptability deteriorate when reusing facts across different contexts
Solution Approach 1:
The patent segments the knowledge graph into two independent components: a static content storage layer (persisting facts and entities) and a dynamic graph construction layer (creating context-specific graphs). This segmentation allows the same content records to be reused across multiple contexts without requiring reorganization, thereby improving adaptability while maintaining simple maintenance of the underlying data structure
Solution Approach 2:
The patent introduces dynamic graph construction where the graph structure (nodes and edges) is created on-demand in memory based on specific contexts or queries, rather than maintaining a fixed schema. The system dynamically determines which content records to include and how to connect them, enabling flexible adaptation to different use cases without modifying the underlying storage structure
2Adaptability or versatility
If knowledge graphs are reorganized to accommodate different contexts, then versatility improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary organization of content records into a standardized, context-agnostic storage format that captures all relevant facts and entities. By pre-structuring the content storage layer with consistent schemas for entities, properties, and relationships, the system eliminates the need for time-consuming reorganization when creating graphs for different contexts - the dynamic construction layer simply queries and connects existing records based on context-specific criteria
3Reliability
If detailed content records are stored for all possible contexts, then completeness improves, but storage requirements increase
Solution Approach 1:
The patent merges multiple context-specific knowledge graphs into a single unified content storage layer. Instead of maintaining separate storage structures for each context or application, the system consolidates all facts, entities, and relationships into one standardized content repository. This consolidation ensures data completeness across all contexts while avoiding redundant storage, as the same content records can serve multiple graph construction purposes
Data Source
AI summary
In an approach for a dynamic in-memory construction of a knowledge graph structure, the knowledge graph structure comprising a plurality of nodes and edges linking selected nodes to each other, a processor receives a record comprising a plurality of strings. The plurality of strings relates to a command combined with a set of strings. A processor determines content records relating to nodes relating to each of the strings. A processor assigns node identifiers for respective determined content records. A processor appends the node identifiers to a dynamic in-memory knowledge graph structure. A processor modifies an edge between selected ones of the node identifiers based on the command combined with the set of strings. A processor builds the dynamic in-memory knowledge graph structure.