Knowledge Graph Mining for Interactive Topic Discovery
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Solution Overview
Problem
Existing knowledge base systems underutilize data and knowledge by failing to effectively connect and visualize related topics, leading to limited discovery and exploration of information.
Innovation Solution
A system that mines a knowledge base to create a knowledge graph, incorporating both semantic context and user personalized results, to visualize related topics and enable users to interactively discover additional context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional knowledge base systems store data without active mining and visualization, then data storage is simple and straightforward, but data utilization is low and knowledge discovery is limited
Solution Approach 1:
The system performs preliminary mining of the knowledge base to identify and extract related topics before they are needed by users. By proactively discovering and pre-organizing knowledge relationships, the system enables users to access relevant information more efficiently without requiring complex manual search processes.
Solution Approach 2:
The patent introduces an intermediary layer between raw data storage and user access. This intermediary component (the mining and visualization system) processes stored data into actionable knowledge graphs and visualizations, bridging the gap between simple data storage and effective knowledge discovery.
2Measurement precision
If the system provides personalized results based on user profiles, then user relevance and accuracy improve, but system complexity increases due to additional processing requirements
Solution Approach 1:
The system applies local quality by tailoring knowledge graph visualizations and recommendations to individual user preferences and profiles. Instead of providing uniform results to all users, the system adjusts the presentation and filtering of related topics based on each user's specific interests and historical data, improving relevance without requiring complete system redesign.
3Quantity of substance
If the knowledge base stores large volumes of data, then comprehensive information availability increases, but data discovery and exploration become more difficult
Solution Approach 1:
The system segments the large knowledge base into manageable portions by identifying and extracting specific related topics through mining algorithms. Instead of presenting users with the entire data volume, the system divides the knowledge base into relevant subsets based on semantic relationships and user context, making data discovery more manageable and less overwhelming.
Solution Approach 2:
The patent transforms the data presentation from a flat, two-dimensional search interface to a three-dimensional knowledge graph visualization. By adding the dimension of relational connectivity and hierarchical organization, the system makes vast amounts of data more navigable and discoverable through visual patterns and interactive exploration rather than linear search.
Data Source
AI summary
To mine and visualize related topics in a knowledge base, where a knowledge base is mined for related topics to create a knowledge graph that is output as a visualization display of automatically suggested related topics. To mine the knowledge base an approach has been developed which incorporates user personalized results in addition to semantic context. The results are displayed in a visualization display for user interaction. While interacting with a suggested topic the user can view and select related topic information which enables users to discover other similar or related topics they would be interested in gaining additional context about. Thus, the related topics and visualization display according to aspects described herein may serve the purpose of more effective utilization and exploration of the knowledge base.