Knowledge Graph Pruning via Sampling and Information Gain
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Solution Overview
Problem
Enterprise data storage systems using knowledge graphs face inefficiencies in query analysis and resource utilization due to the inclusion of irrelevant data, leading to high computing resource demands and low information gain in query results.
Innovation Solution
A knowledge graph system employing graph pruning, schema pruning, and query correlation techniques to dynamically remove irrelevant information and optimize data storage, utilizing graph sampling and information gain calculations to construct a smaller, more relevant knowledge graph for faster query processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire knowledge graph dataset is analyzed for query processing, then comprehensive and accurate query results are obtained, but computing resource consumption increases significantly
Solution Approach 1:
The knowledge graph is segmented into multiple partitions or subsets, allowing query processing to be distributed across different segments. This enables selective analysis of only relevant portions of the knowledge graph for each query, reducing overall computing resource consumption while maintaining result accuracy through systematic coverage of necessary segments.
Solution Approach 2:
Irrelevant or low-value entities and relationships are extracted and removed from the knowledge graph through pruning operations. This extraction of unnecessary elements reduces the dataset size that requires analysis during query processing, thereby decreasing computing resource consumption while preserving the integrity of relevant information needed for accurate results.
2Loss of information
If comprehensive data is stored in the knowledge graph, then information completeness is maintained, but storage requirements increase
Solution Approach 1:
Redundant, duplicate, or low-value information is extracted and removed from the knowledge graph through pruning techniques. This extraction process reduces storage requirements by eliminating unnecessary data while maintaining the completeness of essential information needed for query operations.
Solution Approach 2:
The system dynamically adjusts parameters such as pruning thresholds, confidence scores, and relevance weights based on query characteristics and usage patterns. This allows the knowledge graph to adapt its information retention strategy, maintaining completeness for high-value information while reducing storage for low-priority data.
3Measurement precision
If the entire knowledge graph is processed for every query, then thorough analysis is achieved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing metadata, statistics, and relevance scores for knowledge graph entities and relationships. During query processing, this pre-prepared information enables rapid filtering and identification of relevant subsets, achieving thorough analysis of necessary portions without processing the entire knowledge graph, thereby reducing processing time.
Solution Approach 2:
The knowledge graph processing approach is made dynamic by adapting the scope and depth of analysis based on query characteristics, user preferences, and real-time performance metrics. This allows the system to adjust processing thoroughness dynamically, maintaining high analysis quality for critical queries while reducing processing time for less demanding operations.
Data Source
AI summary
Knowledge graph systems are disclosed for implementing multiple approaches, including stand alone or combined approaches, for knowledge graph pruning. The approaches are based on graph sampling work such as, for example, information gain theory. The approaches are applied by a knowledge graph system to perform schema pruning, automatic graph pruning, and query correlation for improving query performance.