Knowledge Graph Refinement via Automated Cluster Analysis
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Solution Overview
Problem
Knowledge graphs often suffer from over-specification and under-specification, leading to inefficient data modeling and resource allocation, as existing methods require manual analysis or specialized knowledge to detect and correct these issues.
Innovation Solution
A computer-implemented method that automatically detects under-specification by determining sub-classes based on latent hierarchical or peer-to-peer relationships in a knowledge graph, and addresses over-specification by consolidating entities into a single cluster using a Voronoi cells cluster initialization formula, enabling refinement without the need for specialized domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis or specialized knowledge is used to detect and correct over-specification and under-specification, then detection accuracy is improved, but operation complexity and time consumption increase
Solution Approach 1:
The system performs self-diagnosis by automatically detecting over-specification and under-specification in knowledge graphs through computational algorithms. The knowledge graph system itself identifies its own specification issues without requiring external manual analysis, thereby resolving the contradiction between detection accuracy and operational simplicity.
Solution Approach 2:
The system uses cluster size parameters and density metrics to automatically detect specification issues. By monitoring these quantitative parameters, the system achieves accurate detection without requiring specialized domain knowledge, thus improving ease of operation while maintaining detection precision.
2Measurement precision
If manual analysis or specialized knowledge is used to detect and correct over-specification and under-specification, then detection accuracy is improved, but time consumption increases
Solution Approach 1:
The knowledge graph system automatically performs self-diagnosis and detection of specification issues through embedded computational algorithms, eliminating the need for time-consuming manual analysis by specialists while maintaining high detection accuracy.
Solution Approach 2:
The system continuously monitors cluster sizes and density metrics in real-time, performing preliminary detection of specification issues before they become problematic. This proactive approach reduces the time required for detection compared to reactive manual analysis.
3Ease of operation
If automated methods are used to detect over-specification and under-specification, then ease of operation is improved, but detection accuracy may deteriorate
Solution Approach 1:
The automated system uses well-defined quantitative parameters such as cluster size thresholds and density metrics to detect specification issues. These objective parameters enable accurate automated detection without requiring specialized knowledge, thus maintaining both ease of operation and detection accuracy.
Solution Approach 2:
The system replaces manual expert analysis with computational algorithms that apply consistent mathematical criteria for detection. This substitution maintains or improves detection accuracy while dramatically improving ease of operation by eliminating the need for specialized human expertise.
4Measurement precision
If knowledge graphs are over-specified, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system dynamically adjusts the specification level of the knowledge graph by automatically detecting and correcting over-specification. When over-specification is detected, the system consolidates entities and reduces unnecessary detail, thereby reducing complexity while maintaining adequate measurement precision for the application.
5Device complexity
If knowledge graphs are under-specified, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system dynamically adjusts the specification level by detecting under-specification through cluster analysis and automatically adding necessary sub-classes and entities. This ensures the knowledge graph maintains adequate measurement precision while avoiding unnecessary complexity through targeted rather than comprehensive specification.
Data Source
AI summary
Systems and methods for automated resolution of over-specification and under-specification in a knowledge graph are disclosed. In embodiments, a method includes: determining, by a computing device, that a size of an object cluster of a knowledge graph meets a threshold value indicating under-specification of a knowledge base of the knowledge graph; determining, by the computing device, sub-classes for objects of the knowledge graph; re-initializing, by the computing device, the knowledge graph based on the sub-classes to generate a refined knowledge graph, wherein the size of the object cluster is reduced in the refined knowledge graph; and generating, by the computing device, an output based on information determined from the refined knowledge graph.