User-Defined Knowledge Graph Alerts for Relevant Changes
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Solution Overview
Problem
Existing alert systems for relevant information suffer from 'goldilocks syndrome', where users either receive too much or too little information, making it difficult to identify the most relevant content, leading to missed important updates.
Innovation Solution
A user-defined intent is registered to monitor changes in a knowledge graph, triggering alerts for relevant updates, with a listener analyzing the graph to identify changes and generating messages for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a user subscribes to all relevant newsletters, then the user receives complete information coverage, but the user experiences information overload and cannot read all newsletters
Solution Approach 1:
The system segments information by creating user-specific intent profiles that divide the broad newsletter content into personalized subsets. Each user receives only the segmented portion of information that matches their defined intents, rather than receiving all information in its undivided form.
Solution Approach 2:
The system applies local quality by tailoring the information content to each user's specific needs and preferences. Different users receive different qualities and types of information based on their individual intent definitions, rather than receiving uniform information coverage.
2Quantity of substance
If a user subscribes to a subset of newsletters, then the user reduces information overload, but the user misses important information
Solution Approach 1:
The system uses feedback mechanisms where user responses to alerts and engagement patterns are continuously monitored. This feedback refines the intent profiles and adjusts the information filtering criteria, ensuring that important information is captured while maintaining manageable volume.
Solution Approach 2:
The system performs preliminary action by proactively analyzing knowledge graph changes against user intents before generating alerts. This preliminary filtering ensures that only potentially important information matching user interests is selected for alert generation, preventing both overload and missed information.
3Loss of information
If traditional alert systems provide comprehensive information, then all relevant updates are captured, but users cannot identify the most relevant content
Solution Approach 1:
The system extracts and isolates only the specific information elements that match user-defined intents from the comprehensive knowledge graph. By taking out only the relevant portions rather than presenting all information, the system makes relevance identification straightforward while maintaining completeness of important updates.
4Loss of information
If users spend time scanning newsletters to find relevant information, then they may identify important content, but they have less time for actual job functions
Solution Approach 1:
The system performs self-service by automatically monitoring the knowledge graph, detecting changes, matching them against user intents, and generating personalized alerts. This eliminates the need for users to manually scan newsletters, as the system serves itself in identifying and delivering relevant information.
Solution Approach 2:
The system performs preliminary action by pre-processing and filtering information before delivery. Changes are detected and filtered against user intents in advance, so when information is presented to the user, it has already been prepared and organized for immediate relevance assessment, saving user time.
Data Source
AI summary
Techniques and solutions are provided for providing alerts to users when information changes, particularly information associated with a knowledge graph. A user can define an intent, where the intent describes the type of information for which a user desires to receive alerts. The intent can be specified directly with respect to knowledge graph elements, or the intent can be specified in another manner and mapped to such elements. A listener is implemented for the intent. A knowledge graph is periodically reviewed for updates. Updates that are relevant to a particular user intent cause the associated listener to be triggered, and information regarding the update is then provided to the user.