Graph Neural Network Embeddings for Domain-General Knowledge Graph Prediction
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Solution Overview
Problem
Existing graph neural networks for knowledge graphs in dynamic industrial environments lack integration of specialized domain knowledge, leading to inefficient and resource-intensive manual supplementation of missing attribute and relationship information, and suboptimal prediction accuracy.
Innovation Solution
A method to generate a graph neural network by integrating target domain knowledge, using categorical embeddings and node embeddings to create a domain-general model that understands the semantic and contextual information of the target domain, and a prediction head is added for specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph neural networks are used to process knowledge graphs in dynamic industrial environments, then the ability to represent and reason knowledge is improved, but the lack of domain knowledge integration leads to suboptimal prediction accuracy
Solution Approach 1:
The patent applies preliminary action by pre-training the graph neural network model on source domain knowledge graphs to learn general structural patterns and relationships. This pre-training phase prepares the model beforehand so that when applied to target domains, it already possesses foundational knowledge, reducing the need for extensive domain-specific retraining and improving prediction accuracy from the outset
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting model parameters, embedding dimensions, and architectural configurations based on the specific characteristics of different target domains. This allows the same base model to adapt to various domains (molecular graphs, social networks, etc.) by modifying parameters rather than requiring complete retraining, thus improving both accuracy and domain adaptability
2Loss of information
If manual supplementation of missing attribute and relationship information is performed, then the completeness of knowledge graphs is improved, but the process becomes inefficient and resource-intensive
Solution Approach 1:
The patent applies self-service by enabling the graph neural network model to automatically generate and supplement missing attribute information and relationship data within knowledge graphs. Instead of requiring manual intervention, the model performs self-supervised learning where it predicts missing nodes, edges, and attributes based on existing graph structures and patterns, thereby completing the knowledge graph autonomously and efficiently
Solution Approach 2:
The patent replaces the mechanical manual process of knowledge graph supplementation with an automated computational system. The graph neural network model uses learned patterns and representations to automatically infer and add missing information, substituting human manual work with an efficient algorithmic process that can handle large-scale knowledge graphs without proportional increases in resources
3Measurement precision
If domain-specific knowledge is integrated into graph neural networks, then prediction accuracy is improved, but the model complexity and retraining requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the knowledge integration process into distinct modules: a base graph neural network component that handles general graph structures, and domain-specific adapter components that inject targeted domain knowledge. This modular segmentation allows domain knowledge to be integrated without completely redesigning the entire model, managing complexity through organized, separable functional units
Solution Approach 2:
The patent implements universality by designing a domain-adaptive graph neural network framework that can serve multiple domains through a shared base architecture. The model uses universal components (graph convolutional layers, attention mechanisms) that can be applied across different domain types, with only lightweight domain-specific adaptations needed, thereby improving prediction accuracy across domains without proportionally increasing overall model complexity
4Adaptability or versatility
If graph neural networks are trained on multiple domains, then the model becomes more versatile, but the computing resources and training time increase
Solution Approach 1:
The patent applies preliminary action by performing pre-training on source domains or general graph structures before fine-tuning on specific target domains. This preliminary phase establishes a robust base model that captures general graph patterns, reducing the computational burden of subsequent domain-specific training and enabling efficient adaptation to multiple domains without requiring exhaustive training on all of them simultaneously
Solution Approach 2:
The patent implements discarding and recovering by selectively discarding domain-specific parameters during transfer learning while retaining the general graph processing capabilities. When adapting to new domains, the model discards overly specific learned parameters that may not generalize, then recovers and relearns appropriate domain-specific features through efficient fine-tuning, thereby managing computing resources effectively across multiple domains
Data Source
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AI summary
Examples of the present disclosure involve a generation method, a usage method, a device, an electronic device, and a computer program product of a graph neural network. The generation method comprises generating at least one categorical embedding based on target domain knowledge. The generation method further comprises determining node embeddings of the training graph using at least one categorical embedding and original node features of the training graph. The generation method further comprises generating a graph neural network using the determined node embeddings of the training graph. The method of generating a graph neural network consistent with examples of the present disclosure are capable of infusing domain knowledge into a graph neural network such that the graph neural network understands the domain knowledge. In this way, the graph neural network can understand the true semantic and contextual information of the target domain, thus granting it domain generality.