International business machines corporation (20240281648). PERFORMING SEMANTIC MATCHING IN A DATA FABRIC USING ENRICHED METADATA simplified abstract
Contents
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 20240281648 titled 'PERFORMING SEMANTIC MATCHING IN A DATA FABRIC USING ENRICHED METADATA
Simplified Explanation: The patent application describes a method, system, and computer program for semantic matching in a data fabric using knowledge graphs enriched with metadata and master data.
- Trained multi-layer graph neural network generates embeddings based on knowledge graphs with enriched metadata.
- Behavioral metadata from data stewards is collected and used to further enrich metadata in knowledge graphs.
- Semantic matching of data assets in the data fabric is performed using the embeddings generated by the neural network.
- Master data and behavioral metadata are utilized to enhance the effectiveness of semantic matching in the data fabric.
Key Features and Innovation: - Utilization of knowledge graphs enriched with metadata and master data - Trained multi-layer graph neural network for generating embeddings - Monitoring and collection of behavioral metadata from data stewards - Semantic matching of data assets in the data fabric using embeddings - Enhanced effectiveness of semantic matching through the use of master data and behavioral metadata
Potential Applications: This technology can be applied in various industries such as finance, healthcare, e-commerce, and more for improved data management and analysis.
Problems Solved: - Enhances semantic matching in data fabrics - Improves data asset organization and retrieval - Utilizes behavioral metadata for better data enrichment
Benefits: - Increased efficiency in data matching processes - Enhanced data quality and accuracy - Improved decision-making based on enriched data insights
Commercial Applications: Potential commercial applications include data integration platforms, data governance solutions, and data analytics tools for businesses looking to optimize their data management processes.
Questions about Semantic Matching in Data Fabric: 1. How does the use of master data and behavioral metadata improve semantic matching in a data fabric? 2. What are the key advantages of utilizing a multi-layer graph neural network for generating embeddings in this context?
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.
