Semantic Network Cell Weighting for Heterogeneous Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine learning techniques based on neural networks are limited to specific use cases and cannot effectively handle semantic networks with heterogeneous data, lacking elements of weighted couplings and memory decay over time, which restricts their applicability and efficiency.
Innovation Solution
A cognitive machine learning system that generates and operates a semantic network with attributes like weight, access count, and latest access time, emulating memory reinforcement and loss through semantic network cell weight rules, enabling efficient search and optimization of the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine learning techniques based on neural networks are used, then training data can be processed, but the system is restricted to specific and narrow use cases and cannot handle heterogeneous data effectively
Solution Approach 1:
The patent applies universality by creating a semantic network data model that can handle multiple types of data and use cases through a unified structure. The semantic network uses generic components (nodes, edges, weights) that can represent various relationships and data types, making the system adaptable to diverse applications without requiring separate specialized models for each use case.
Solution Approach 2:
The patent employs parameter changes by introducing dynamic weight attributes to semantic network cells that can be adjusted based on access patterns and time decay. This allows the system to adapt its behavior and prioritization based on changing conditions, enabling it to handle heterogeneous data effectively through parameter adjustment rather than structural complexity.
2Productivity
If structured data models with paired data series are used, then training instances can be generated, but resource consumption increases for each training instance
Solution Approach 1:
The patent applies self-service through the automatic weight adjustment mechanism that uses time decay and access count metrics. The system automatically reinforces frequently accessed semantic cells and decays infrequently accessed ones without requiring manual intervention or extensive retraining, improving productivity while reducing resource consumption compared to conventional retraining approaches.
Solution Approach 2:
The patent implements periodic action through the time decay mechanism that periodically adjusts weights based on elapsed time since last access. This periodic reinforcement and decay cycle maintains data relevance efficiently without continuous resource-intensive processing, balancing productivity with resource conservation.
3Productivity
If semantic network cells lack weight attributes and memory decay mechanisms, then the structure remains simple, but search efficiency and optimization capability are limited
Solution Approach 1:
The patent applies local quality by introducing weight attributes specifically to semantic network cells that are directly involved in searches and queries. Rather than complicating the entire system structure, the weight mechanism is applied locally to individual cells based on their access patterns, improving search efficiency where needed while maintaining simplicity elsewhere in the network.
Solution Approach 2:
The patent implements feedback through the weight adjustment mechanism that uses access count and time decay information to reinforce or decay semantic cell weights. This feedback loop continuously optimizes the semantic network based on actual usage patterns, improving search efficiency by prioritizing relevant information while adding only minimal structural complexity through the weight attribute.
Data Source
AI summary
Methods, computer program products, and systems are presented. The methods include, for instance: generating a semantic network cell for a component of a semantic expression in a semantic network. The semantic network includes multiple semantic network cells. Each semantic network cell has attributes of a weight, an access count, and a latest time of access. A machine learning process reinforces the semantic network cell by access and deteriorates the semantic network cell over time based on semantic network cell weight rules, while the semantic network is servicing searches.