International business machines corporation (20240242070). Entity Classification Using Graph Neural Networks simplified abstract
Contents
- 1 Entity Classification Using Graph Neural Networks
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Entity Classification Using Graph Neural Networks - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about the Technology
- 1.11 Original Abstract Submitted
Entity Classification Using Graph Neural Networks
Organization Name
international business machines corporation
Inventor(s)
Aniket Saxena of Ghaziabad (IN)
Balaji Ganesan of Bengaluru (IN)
Muhammed Abdul Majeed Ameen of Kozhikode (IN)
Arvind Agarwal of New Delhi (IN)
Soma Shekar Naganna of Bengaluru (IN)
Entity Classification Using Graph Neural Networks - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240242070 titled 'Entity Classification Using Graph Neural Networks
Simplified Explanation
The patent application describes a method for classifying records using a graph neural network trained on subgraphs of matched records.
Key Features and Innovation
- Processor units create a training dataset of subgraphs of matched records.
- Importance of attributes in matched records is identified.
- Subgraphs are related by a subset of attributes.
- Graph neural network classifies records as belonging to a specific entity.
Potential Applications
This technology can be applied in various fields such as fraud detection, customer relationship management, and data analysis.
Problems Solved
- Efficient classification of records based on related attributes.
- Improved accuracy in identifying records belonging to a specific entity.
Benefits
- Enhanced data classification and organization.
- Increased efficiency in data analysis and decision-making processes.
Commercial Applications
- Fraud detection systems in financial institutions.
- Customer segmentation in marketing strategies.
- Data management tools for large organizations.
Questions about the Technology
How does this technology improve data classification processes?
This technology improves data classification by utilizing subgraphs of matched records and identifying the importance of attributes in the classification process.
What are the potential applications of this technology beyond record classification?
This technology can be applied in various fields such as fraud detection, customer relationship management, and data analysis.
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Original Abstract Submitted
a computer implemented method classifies records. a number of processor units creates a training dataset comprising subgraphs of matched records matched to an entity and identifying an importance of attributes in the matched records. the matched records in a subgraph are related to each other by a subset of the attributes. the number of processor units trains a graph neural network using the training dataset. the graph neural network classifies the records as belonging to the entity.
