Personal Knowledge Graph Relevance Prediction via Machine Learning
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Solution Overview
Problem
Ambiguities in human interactions, such as identifying the correct person or application to call, are unresolved due to lack of context-based relevance prediction in existing systems.
Innovation Solution
A personal knowledge graph is used to predict relevance by collecting conditional probabilities from interaction statistics and applying a machine learning model, allowing for context-based ranking of entities like people or applications, thereby resolving ambiguities in user commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a knowledge graph is used to store entity interactions, then the system can maintain context information, but the system cannot predict relevance without additional machine learning components
Solution Approach 1:
The patent combines a knowledge graph (for storing entity interactions and context) with machine learning models (for relevance prediction) into a unified system. The knowledge graph serves as both data storage and feature extraction mechanism, while the ML model processes this structured data to generate relevance predictions, resolving the contradiction by integrating multiple functional components.
Solution Approach 2:
The patent introduces an intermediary layer between the knowledge graph and the user interface. This intermediary processes the raw interaction data from the knowledge graph through machine learning models to generate relevance predictions, which then guide the system's responses. This mediator layer enables the system to leverage context information without directly exposing the complexity of the underlying data structure.
2Measurement precision
If the system provides detailed context-based predictions, then the accuracy of entity identification improves, but the computational resources required increase
Solution Approach 1:
The patent segments the relevance prediction process into multiple components: (1) extracting features from the knowledge graph, (2) processing these features through machine learning models, and (3) generating final relevance predictions. This segmentation allows the system to process complex context information in manageable stages, improving accuracy while controlling computational resource consumption by only processing data when needed.
Solution Approach 2:
The system applies machine learning models selectively rather than universally. It processes context information and generates detailed predictions only when ambiguity detection triggers them, rather than continuously processing all possible entity interactions. This partial action approach maintains high prediction accuracy for critical decisions while reducing overall computational resource consumption.
3Productivity
If the system resolves all ambiguities automatically, then user interaction efficiency improves, but the system may make errors in complex contexts
Solution Approach 1:
The patent implements feedback mechanisms where the system monitors user responses and interaction patterns. When the system makes a relevance prediction, it observes whether the user accepts or corrects the suggestion. This feedback is fed back into the knowledge graph and used to refine future predictions, improving reliability over time while maintaining high interaction efficiency through automated resolution.
Solution Approach 2:
The system dynamically adjusts its level of automation based on context complexity and confidence levels. In simple contexts, the system automatically resolves ambiguities to maximize efficiency. In complex contexts where uncertainty is high, the system becomes more conservative and may seek user clarification or provide multiple options, thereby maintaining reliability while preserving productivity through context-aware automation.
Data Source
AI summary
Aspects of the subject technology provide a relevance prediction based on a database of entities and statistics of past interactions between the entities. A relevance of a target entity in the database given a context of entities in the database may be predicted by deriving a probability set of at least one conditional probability from the database for each of the context entities, and then applying a machine learning model to the probability set to produce the relevance of the target entity.