18155295. Entity Classification Using Graph Neural Networks simplified abstract (International Business Machines Corporation)
Contents
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 18155295 titled 'Entity Classification Using Graph Neural Networks
The abstract describes a computer-implemented method for classifying records using a graph neural network.
- Simplified Explanation: The method involves creating a training dataset of matched records related to an entity, training a graph neural network, and classifying records based on their relationship to the entity.
- Key Features and Innovation:
- Creation of a training dataset comprising subgraphs of matched records - Importance of attributes in matched records identified - Records in a subgraph related by a subset of attributes - Training of a graph neural network by processor units - Classification of records based on their relationship to the entity
- Potential Applications:
- Data classification in various industries - Fraud detection in financial transactions - Customer relationship management in businesses - Healthcare record management - Social network analysis
- Problems Solved:
- Efficient classification of records based on relationships - Identification of important attributes in records - Improved data organization and analysis
- Benefits:
- Enhanced data classification accuracy - Streamlined record management processes - Increased efficiency in data analysis - Better decision-making based on classified data
- Commercial Applications:
- Data analytics software for businesses - Fraud detection tools for financial institutions - Customer relationship management systems - Healthcare data management platforms
- Questions about Graph Neural Network Classification:
1. How does the graph neural network classify records based on their relationship to the entity? 2. What are the potential limitations of using a graph neural network for record classification?
- Frequently Updated Research:
- Stay updated on advancements in graph neural network technology for record classification.
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.
