Knowledge Graph Pre-Training with Structural Context Encoding
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Solution Overview
Problem
Existing knowledge graph pre-training models struggle to automatically capture deep structural context information and adapt to different downstream tasks, requiring separate training for various knowledge graph tasks.
Innovation Solution
A pre-training method using a model comprising a triple integration module, structural information module, and general task module to encode context triples, generating optimized structural representation vectors for triples, which can be fine-tuned for specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate training is performed for various knowledge graph tasks, then task-specific performance can be optimized, but training time and computational resources increase significantly
Solution Approach 1:
The patent applies pre-training on a large-scale knowledge graph corpus before fine-tuning for specific downstream tasks. The pre-training phase learns general structural context information and representation patterns that can be transferred to multiple tasks, eliminating the need for separate training from scratch for each task while maintaining task-specific performance through subsequent fine-tuning.
Solution Approach 2:
The patent designs a universal pre-trained model that can serve multiple knowledge graph downstream tasks including entity typing, link prediction, and entity alignment. The model learns task-agnostic structural representations that are applicable across different tasks, allowing a single model to perform multiple functions without requiring task-specific architectures.
2Measurement precision
If deep structural context information is captured for all knowledge graph tasks, then representation quality improves, but model complexity and computational overhead increase
Solution Approach 1:
The patent extracts and focuses on structural context information from the knowledge graph, specifically the triple structure (head entity, relation, tail entity) and their contextual relationships. By isolating and emphasizing this structural information rather than processing all possible features, the model achieves high representation quality while controlling complexity through targeted feature extraction.
Solution Approach 2:
The patent segments the knowledge graph into discrete triples with structured context information, processing each triple independently through the pre-trained model. This segmentation allows the model to capture deep structural relationships in a manageable, modular way, avoiding the complexity of processing the entire graph structure at once while maintaining comprehensive structural understanding.
3Productivity
If pre-training is performed once on a unified model, then training efficiency improves, but adaptability to different downstream tasks with different structural features may decrease
Solution Approach 1:
The patent employs a dynamic fine-tuning mechanism where the pre-trained model is adapted to different downstream tasks by adjusting its parameters based on task-specific requirements. The model maintains its pre-trained structural understanding while dynamically adapting to different task structures through fine-tuning, achieving both efficiency from pre-training and adaptability for specific tasks.
Data Source
AI summary
Disclosed in the present invention is a knowledge graph pre-training method based on structural context information, the method comprising: for a target triple, constructing an instance comprising context triples, and adopting a triple integration module to encode each of the context triples in the instance to obtain an integration vector; combining the integration vectors for all context triples in the instance into a context vector sequence, and adopting a structural information module to encode the context vector sequence to obtain a structural representation vector for the triple; adopting a general task module to calculate the structural representation vector for the triple, and obtaining a label prediction value for the triples, updating the structural representation vector for the triple based on cross-entropy loss of the label prediction value for the triple and a label truth value for the triple until the completion of the training, so as to obtain an optimized structural representation vector for the target triple. The structural representation vector for the triple obtained by this method incorporates the context information.