Cognitive Memory Encoding for Graph Semantic Indexing
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Solution Overview
Problem
Current graph storage and retrieval methods, such as Google's Page-Rank and other graph algorithms, fail to effectively capture analogies and dynamic changes in networks, especially in large-scale and diverse graph structures, leading to inefficiencies in querying and indexing.
Innovation Solution
The use of a Contraction Rule to identify the largest k-dimensional complex in combinatorial maps, producing a sequence of hierarchical representations that encode graphs as Cognitive Signatures, allowing for dynamic evolution and efficient retrieval by combining topological and geometric measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional graph storage methods (Page-Rank, hashing, content-based access) are used, then network indexing can be performed, but the methods fail to capture analogies and dynamic changes in networks, leading to inefficient querying and indexing
Solution Approach 1:
The patent segments the graph encoding process into multiple hierarchical levels through the Contraction Rule, creating a multi-scale representation system. This allows the system to capture both local structures and global patterns separately, improving the ability to represent dynamic changes while maintaining indexing efficiency through hierarchical organization.
Solution Approach 2:
The patent introduces dynamic capabilities by enabling graphs to evolve through contraction and expansion operations. The system can dynamically adjust the level of abstraction and reconfigure graph representations in response to changing data, allowing efficient capture of dynamic changes while maintaining reliable analogy detection through preserved topological invariants.
2Quantity of substance
If graph-based storage and retrieval methods are used for large-scale data, then network analysis is possible, but the complexity of graph-based methods increases with scale, size, resolution, and number of images or data elements
Solution Approach 1:
The patent applies segmentation by dividing the graph representation into hierarchical levels through repeated application of the Contraction Rule. This creates a multi-scale decomposition where each level represents a simplified version of the graph, allowing the system to handle large-scale data by processing manageable hierarchical segments rather than monolithic graph structures.
Solution Approach 2:
The patent extracts essential topological invariants and semantic properties from complex graph structures through the Contraction Rule. By systematically removing redundant information and retaining only the essential structural relationships, the system reduces the complexity burden while maintaining the ability to analyze large-scale networks.
3Adaptability or versatility
If static graph indexing methods are used, then structured data can be stored, but the methods cannot handle structural variability, complexity, diversity and features that are widely differing or dynamically changing
Solution Approach 1:
The patent implements dynamics by enabling the graph representation to evolve through contraction and expansion operations. The system can dynamically adjust the level of abstraction and reconfigure graph representations in response to changing data structures, providing adaptability to structural variability while managing complexity through controlled transformation processes.
Solution Approach 2:
The patent applies parameter changes by systematically varying the level of contraction and the characteristics of graph representations. The Contraction Rule allows the system to change structural parameters (such as level of abstraction, granularity, and topological fidelity) to match the specific requirements of different data types and query patterns, enhancing versatility without proportionally increasing complexity.
Data Source
AI summary
The invention provides a fast approximate as well as exact hierarchical network storage and retrieval system and method for encoding and indexing graphs or networks into a data structure called the Cognitive Signature for property based, analog based or structure or sub-structure based search. The system and method produce a Cognitive Memory from a multiplicity of stored Cognitive Signatures and are ideally suited to store and index all or parts of massive data sets, linguistic graphs, protein graphs, chemical graphs, graphs of biochemical pathways, image or picture graphs as well as dynamical graphs such as traffic graphs or flows and motion picture sequences of graphs. The system and method have the advantage that properties of the Cognitive Signature of the graph can be used in correlations to the properties of the underlying data making the system ideal for semantic indexing of massive scale graph data sets.