Temporal Graph Completion with Enhanced Representations for Sparse Entities
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Solution Overview
Problem
Conventional temporal knowledge graph completion systems suffer from inflexibility, inaccuracy, and inefficiency due to their inability to accommodate new or sparse entities, leading to poor performance in graph completion tasks and catastrophic forgetting of model parameters.
Innovation Solution
The enhanced graph completion system employs a model-agnostic enhancement layer to generate enhanced entity representations based on both connection-based and relationship-based similarities, and uses weighted frequency-based sampling to boost the visibility of sparse entities during training. This system is trained using a continual learning framework to incrementally update model parameters in response to changes in the temporal knowledge graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional models use local neighborhood proximity view for entity representation, then model complexity is reduced, but adaptability to new or sparse entities deteriorates
Solution Approach 1:
The entity representation is segmented into multiple components: local neighborhood proximity view (connection-based) and global definition view (relationship-based). This segmentation allows the model to handle different types of entity relationships separately, improving adaptability to new entities while maintaining manageable model complexity through modular architecture.
Solution Approach 2:
The patent transitions from a single-dimensional local neighborhood view to a multi-dimensional representation that includes both connection-based similarity (local) and relationship-based similarity (global). This dimensional expansion enables the model to capture diverse entity relationships, improving adaptability without proportionally increasing complexity.
2Measurement precision
If conventional systems use sampling methods that favor well-connected entities, then training efficiency is improved, but accuracy for new or sparse entities deteriorates
Solution Approach 1:
The sampling method applies local quality by differentiating treatment based on entity connectivity. Well-connected entities receive standard sampling weight, while sparsely-connected or new entities receive boosted sampling weights. This localized adjustment ensures accurate representation of all entity types during training while maintaining overall training efficiency.
Solution Approach 2:
The sampling strategy preemptively counteracts the bias toward well-connected entities by applying inverse frequency weighting. This preliminary anti-action ensures that sparsely-connected and new entities are adequately represented in training data, improving accuracy for these entities without significantly reducing training efficiency.
3Adaptability or versatility
If conventional systems fine-tune model parameters to accommodate changes, then adaptability to temporal changes is improved, but reliability deteriorates due to catastrophic forgetting
Solution Approach 1:
The system performs preliminary action by continuously maintaining an updated temporal knowledge graph and pre-computing entity representations before training. This preliminary preparation allows the model to adapt to temporal changes through incremental updates rather than complete retraining, improving adaptability while preserving performance on previously learned tasks through careful update scheduling.
Solution Approach 2:
The patent implements continuity of useful action through incremental training that continuously updates model parameters without interrupting the learning process. By maintaining continuous updates based on new temporal data while preserving old knowledge, the system achieves adaptability to temporal changes without catastrophic forgetting, improving both reliability and adaptability simultaneously.
4Reliability
If conventional systems retrain models entirely to avoid catastrophic forgetting, then reliability is improved, but productivity deteriorates due to high computational demands
Solution Approach 1:
The system applies partial action by updating only the necessary portions of the model parameters rather than retraining the entire model. This partial updating approach maintains reliability on previously learned tasks while significantly reducing computational demands, improving productivity. The model updates only the temporal adaptation components while preserving the core entity representations.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate predicted relationships for entities of a temporal knowledge graph using enhanced entity representations. For instance, in one or more embodiments, the disclosed systems generate a query for predicting a relationship for a subject entity represented within a temporal knowledge graph. The disclosed systems further determine an enhanced entity representation generated for the subject entity by an enhancement layer of a temporal knowledge graph completion model, the enhanced entity representation including a combination of a connection-based similarity for the subject entity and a relationship-based similarity for the subject entity. Using the temporal knowledge graph completion model and based on the enhanced entity representation of the subject entity, the disclosed systems generate a predicted relationship for the subject entity.