User-Specific Knowledge Graphs for Personalized Query Handling
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Solution Overview
Problem
Current search systems lack the ability to provide personalized and relevant results to users by failing to capture user-specific knowledge in a structured manner, leading to inefficiencies in handling complex queries and integrating user activities across different services.
Innovation Solution
The implementation of user-specific structured graphs that process user content to identify entities and relationships, creating a knowledge graph specific to each user, which is then used to provide relevant results and predictions based on user interactions across various computer-implemented services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user-specific structured graphs are implemented to capture user knowledge, then query handling capability and result personalization are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments user knowledge into structured graphs organized by entity types and relationships, dividing complex user data into manageable components that can be processed independently. Each user-specific graph captures particular aspects of user knowledge, allowing the system to handle different query types through specialized graph structures.
Solution Approach 2:
Structured graphs serve as intermediary representations between raw user data and query processing mechanisms. The graphs act as mediators that transform unstructured user interactions into organized knowledge structures, enabling efficient query handling without requiring complex direct processing of raw data.
2Loss of information
If user-specific knowledge graphs are created for each user, then result personalization and relevance are improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing to build user-specific structured graphs from user interactions and content in advance, before queries are submitted. This preliminary structuring of user knowledge enables faster query processing, as the graphs are already organized and ready for efficient searching and matching when queries arrive.
Solution Approach 2:
The system creates simplified copies or representations of user knowledge in structured graph format, which are lighter and faster to process than the original raw user data. These graph representations capture essential user knowledge while reducing computational complexity for query processing.
3Manufacturing precision
If parsers specific to schemas are used to identify entities and relationships, then knowledge graph accuracy and structure are improved, but processing complexity and schema maintenance requirements increase
Solution Approach 1:
The system employs universal parsing mechanisms that can handle multiple schemas through a common framework. Rather than requiring separate complex parsers for each schema, the system uses a unified approach that adapts to different schemas, reducing processing complexity while maintaining accuracy through standardized entity and relationship identification.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving user-specific content, the user-specific content being associated with a user of one or more computer-implemented services, processing the user-specific content using one or more parsers to identify one or more entities and one or more relationships between entities, a parser being specific to a schema, and the one or more entities and the one or more relationships between entities being identified based on the schema, providing one or more user-specific knowledge graphs, a user-specific knowledge graph being specific to the user and including nodes and edges between nodes to define relationships between entities based on the schema, and storing the one or more user-specific knowledge graphs.