Distribution Nodes for Faster Graph-Based Knowledge Inference
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Solution Overview
Problem
Graph-based knowledge systems face performance issues due to the large number of nodes, which impact inference and inductive reasoning speed, especially when integrating open data from external sources with unknown formats.
Innovation Solution
The system condenses multiple inductive nodes into a single distribution node using statistical distribution models, allowing for faster traversal and inference by representing characteristics of the nodes with mean and standard deviation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple inductive nodes are retained in the graph data structure to represent knowledge entities, then the completeness and detail of knowledge representation is improved, but the inference and inductive reasoning speed deteriorates due to the large number of nodes
Solution Approach 1:
The patent merges multiple inductive nodes that share common attributes into a single distribution node. Instead of maintaining separate nodes for each knowledge entity with similar characteristics, the system combines them and represents their collective attributes using statistical distribution parameters (mean and standard deviation). This merging reduces the total node count in the graph while preserving the essential knowledge representation, thereby improving inference speed without significant loss of information.
Solution Approach 2:
The patent transforms the representation of node attributes from discrete individual values to continuous statistical distribution parameters. Each distribution node is characterized by mean and standard deviation parameters that describe the attribute distribution across multiple original nodes. This parameter transformation enables more efficient storage and processing, as the system now manages continuous parameters rather than numerous discrete node instances, resolving the contradiction between information completeness and processing speed.
2Adaptability or versatility
If open data from external sources with unknown formats is integrated into the graph-based knowledge system, then the versatility and coverage of the knowledge system is improved, but the processing complexity and time consumption increases
Solution Approach 1:
The patent creates a universal distribution node structure that can accommodate various types of open data from different external sources regardless of their original formats. The distribution node framework serves multiple functions: it can represent different data types (numeric, categorical, temporal), handle unknown formats through attribute prediction, and integrate heterogeneous data sources into a unified graph structure. This universal representation mechanism enables the system to process diverse data formats efficiently without requiring format-specific processing logic for each data source.
Solution Approach 2:
The patent performs preliminary actions by predicting data types and generating distribution parameters for new observations before full integration into the knowledge system. When new open data arrives, the system predicts the data types of attributes and pre-computes distribution parameters (mean, standard deviation) based on patterns and value ranges. This preliminary processing prepares the data in advance for efficient integration, reducing the time required for actual data incorporation and minimizing processing delays during runtime.
Data Source
AI summary
A computational device generates a graph data structure comprising a plurality of inductive nodes having a plurality of attributes. The plurality of inductive nodes are clustered into a plurality of groupings based on the plurality of attributes. A substitution is made of more than one inductive node with a single distribution node that represents characteristics of the more than one inductive node based on distribution models of attributes included in the plurality of groupings.