17973322. GENERATING AND UPGRADING KNOWLEDGE GRAPH DATA STRUCTURES simplified abstract (SAP SE)
Contents
GENERATING AND UPGRADING KNOWLEDGE GRAPH DATA STRUCTURES
Organization Name
Inventor(s)
Sandra Bracholdt of Dielheim (DE)
Michael Hoerisch of Heidelberg (DE)
GENERATING AND UPGRADING KNOWLEDGE GRAPH DATA STRUCTURES - A simplified explanation of the abstract
This abstract first appeared for US patent application 17973322 titled 'GENERATING AND UPGRADING KNOWLEDGE GRAPH DATA STRUCTURES
The patent application focuses on utilizing relationship data in a computing system, extracting relationship data from a document, determining confidence values, and incorporating feedback to update confidence values.
- Relationship data is extracted from a document and assigned a confidence value.
- The data is then written to a knowledge graph data structure.
- A user interface page is served to gather feedback on the accuracy of the relationship data.
- The confidence value is updated based on the feedback and the trust score of the user.
Potential Applications: - Data analysis and visualization tools - Information retrieval systems - Knowledge management platforms
Problems Solved: - Improving the accuracy of relationship data extraction - Enhancing user feedback mechanisms - Increasing trust in the data presented
Benefits: - Enhanced data reliability and accuracy - Improved user experience and engagement - Streamlined data processing and analysis
Commercial Applications: Title: "Enhanced Relationship Data Processing System" This technology can be applied in industries such as: - Market research - Customer relationship management - Business intelligence
Questions about Relationship Data Processing: 1. How does the system determine the confidence value of extracted relationship data? The confidence value is determined based on the accuracy of the data and user feedback.
2. What role does the trust score of the user play in updating the confidence value? The trust score of the user influences the modification of the confidence value based on feedback.
Frequently Updated Research: Stay updated on advancements in relationship data processing algorithms and user feedback integration for improved data accuracy and trustworthiness.
Original Abstract Submitted
Various examples are directed to systems and methods for utilizing relationship data in a computing system. The computing system may extract first relationship data from a document and determine a first confidence value describing the first relationship data. The computing system may write the first relationship data to a knowledge graph data structure. The computing system may serve a first user interface page to a user computing device associated with a first user and receive feedback data describing an accuracy of the first relationship data. The computing system may modify a first confidence subunit of a triple data unit associated with the relationship to describe an updated confidence value based on the feedback data and a trust score of the first user.
