18472570. ONBOARDING OF ENTITY DATA simplified abstract (Google LLC)

ONBOARDING OF ENTITY DATA

Organization Name

Google LLC

Inventor(s)

Ion Constantinescu of Zurich (CH)

ONBOARDING OF ENTITY DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 18472570 titled 'ONBOARDING OF ENTITY DATA

Simplified Explanation

The patent application describes techniques to improve onboarding of third party entity data with existing knowledge graphs.

• A computing system associated with an existing knowledge graph receives a request from a third party to onboard a plurality of entities with associated identifiers and relationships.
• First third party entity data is received from the third party, describing the entities and their identifiers/relationships.
• The data is analyzed to identify semantic fingerprints matching subsets of the entities, and results are determined based on the analysis.
• Remedial actions may be triggered based on the failure statistic of applying rules to subsets of entities with matching semantic fingerprints.

Potential Applications

This technology could be applied in various industries such as finance, healthcare, and e-commerce for efficient integration of third party entity data into existing knowledge graphs.

Problems Solved

1. Streamlining the process of incorporating third party entity data into existing knowledge graphs. 2. Enhancing data accuracy and consistency by identifying semantic fingerprints and applying rules accordingly.

Benefits

1. Improved data integration and management. 2. Enhanced data quality and reliability. 3. Increased efficiency in onboarding processes.

Potential Commercial Applications

Optimizing data integration processes in industries such as financial services, healthcare systems, and e-commerce platforms.

Possible Prior Art

There may be prior art related to data integration techniques, semantic analysis, and knowledge graph management systems that could be relevant to this patent application.

How does this technology handle data privacy and security concerns?

The patent application does not specifically address the measures taken to ensure data privacy and security when integrating third party entity data with existing knowledge graphs. It would be important to understand the protocols in place to protect sensitive information.

What scalability challenges might arise when implementing this technology in large-scale systems?

The patent application does not delve into the scalability aspects of the proposed techniques. It would be crucial to investigate how the system handles a large volume of entity data and the associated identifiers/relationships to ensure efficient performance in real-world applications.

Original Abstract Submitted

Techniques are described herein to improve onboarding of third party entity data with existing knowledge graphs. In various implementations, a computing system associated with an existing knowledge graph may receive a request from a third party to onboard, with the existing knowledge graph, a plurality of entities. Each entity may have associated identifier(s) and relationship(s) with other entities of the plurality of entities. First third party entity data that describes the plurality of entities and associated identifiers/relationships may be received from the third party. The first third entity party data may be analyzed to identify semantic fingerprint(s) matching respective subsets of the entities. Results related to the analyzing may be determined. The results may include a statistic representing success or failure of applying rule(s) to a respective subset of entities that match a given semantic fingerprint. Remedial action(s) may be triggered based on the failure statistic.