18111379. PERFORMING SEMANTIC MATCHING IN A DATA FABRIC USING ENRICHED METADATA simplified abstract (International Business Machines Corporation)
Contents
- 1 PERFORMING SEMANTIC MATCHING IN A DATA FABRIC USING ENRICHED METADATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PERFORMING SEMANTIC MATCHING IN A DATA FABRIC USING ENRICHED METADATA - 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 Semantic Matching in a Data Fabric
- 1.11 Original Abstract Submitted
PERFORMING SEMANTIC MATCHING IN A DATA FABRIC USING ENRICHED METADATA
Organization Name
International Business Machines Corporation
Inventor(s)
Balaji Ganesan of Bengaluru (IN)
Muhammed Abdul Majeed Ameen of Kozhikode (IN)
Aniket Saxena of Ghaziabad (IN)
Arvind Agarwal of New Delhi (IN)
Priyanka Telang of Bengaluru (IN)
Soma Shekar Naganna of Bangalore (IN)
PERFORMING SEMANTIC MATCHING IN A DATA FABRIC USING ENRICHED METADATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18111379 titled 'PERFORMING SEMANTIC MATCHING IN A DATA FABRIC USING ENRICHED METADATA
Simplified Explanation
The patent application describes a method for semantic matching in a data fabric using knowledge graphs enriched with metadata and master data, along with a multi-layer graph neural network to generate embeddings. Behavioral metadata from data stewards is also utilized to enhance the metadata in the knowledge graphs, leading to more effective semantic matching of data assets.
- Knowledge graphs enriched with metadata and master data
- Trained multi-layer graph neural network generates embeddings
- Behavioral metadata from data stewards is collected and used to enrich metadata
- Semantic matching of data assets in the data fabric is performed using embeddings
- Utilizes master data and behavioral metadata for more effective semantic matching
Key Features and Innovation
- Utilization of knowledge graphs enriched with metadata and master data
- Trained multi-layer graph neural network for generating embeddings
- Integration of behavioral metadata from data stewards to enhance metadata
- Semantic matching of data assets in the data fabric using embeddings
- Improved effectiveness of semantic matching through the use of master data and behavioral metadata
Potential Applications
The technology can be applied in various industries such as finance, healthcare, e-commerce, and more for semantic matching of data assets in a data fabric.
Problems Solved
The technology addresses the challenge of effectively matching data assets in a data fabric by utilizing master data and behavioral metadata to enhance the process.
Benefits
- Enhanced semantic matching of data assets
- Improved accuracy and efficiency in data fabric management
- Utilization of master data and behavioral metadata for better insights
Commercial Applications
- Data management systems
- Business intelligence tools
- Data integration platforms
- Knowledge graph applications
Questions about Semantic Matching in a Data Fabric
How does the technology improve the accuracy of semantic matching in a data fabric?
The technology enhances accuracy by utilizing knowledge graphs enriched with metadata, master data, and behavioral metadata to generate embeddings for semantic matching.
What are the potential commercial applications of this technology beyond data fabric management?
The technology can be applied in various industries for tasks such as data integration, business intelligence, and knowledge graph applications.
Original Abstract Submitted
A computer-implemented method, system and computer program product for performing semantic matching in a data fabric. Knowledge graphs are populated with metadata enriched with master data. Based on such knowledge graphs with the metadata enriched with the master data, a trained multi-layer graph neural network generates embeddings. Furthermore, behavioral metadata from data stewards are monitored and collected. Such behavioral metadata may be used to enrich metadata, which are populated in knowledge graphs which are inputted into the multi-layer graph neural network to generate embeddings. Upon generating the embeddings discussed above, semantic matching of the data assets in the data fabric using the embeddings is performed. In this manner, semantic matching of the data assets in the data fabric is more effectively performed by utilizing master data and behavioral metadata.
