Personal Knowledge Graph Integration With Granular Privacy Control
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Solution Overview
Problem
There is a need for a system and method to effectively collect, organize, and manage personal knowledge information for users in a database, including integrating data from various entities of interest while ensuring privacy and security.
Innovation Solution
A Personal Knowledge (PK) graph is generated for a user based on a PK graph model, where the user is represented as a central node connected to entity nodes, with data ingestion and integration from external entities, and permissions and secrecy defined to manage data sharing and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If personal knowledge information is collected and integrated from multiple external entities, then the completeness and value of user data increases, but the complexity of data management and integration increases
Solution Approach 1:
The patent segments personal knowledge information into distinct entities (e.g., contacts, documents, media files) with standardized schemas for each entity type. This segmentation allows complex data from multiple external entities to be organized into manageable, structured components that can be independently processed and integrated into the PK graph without overwhelming complexity
Solution Approach 2:
The patent introduces a standardized entity model and PK graph structure as an intermediary layer between external data sources and the final integrated knowledge base. This intermediary standardizes diverse data formats and relationships, simplifying the integration process while maintaining data completeness from multiple external entities
2Ease of operation
If user data is integrated from multiple entities into a centralized PK graph, then data accessibility and usability improve, but privacy and security risks increase
Solution Approach 1:
The patent implements granular permission controls at the entity and attribute levels within the PK graph, allowing users to specify different access rights for different portions of their personal knowledge data. This local quality approach enables fine-grained privacy management where sensitive attributes can be protected while less sensitive data remains accessible, balancing data usability with privacy protection
Solution Approach 2:
The patent introduces permission models and access control mechanisms as intermediary layers between the integrated PK graph and external access requests. These intermediaries enforce security policies and privacy rules while maintaining data accessibility for authorized users, thereby managing security risks without sacrificing ease of operation
3Productivity
If a standardized entity model is used to ingest data from external entities, then data integration efficiency improves, but the ability to handle diverse entity types may be limited
Solution Approach 1:
The patent designs the entity model with universal, extensible schemas that can accommodate multiple entity types (contacts, documents, media files, etc.) through a common structure. The model uses standardized attribute categories and relationship types that can be applied across diverse entity types, enabling efficient data integration while maintaining the ability to handle variety through configuration rather than requiring completely separate processing logic for each entity type
Data Source
AI summary
Embodiments of the disclosed provide systems and methods for collecting, organizing and managing of personal information for a user and implementing Personal Knowledge (PK) graphs for users in a database system. The system generates a PK graph based on user personal information data for a user and a PK graph model defining a graph data structure with a central node representing the user and one or more nodes representing entities of interest to the user. The system ingests export data from each of the entities of interest based on a model describing the entities. The system integrates the user export data from all the entities of interest into the PK graph to provide an integrated PK graph for the user.