Knowledge Graph Alignment Training With Difficulty-Based Pair Selection
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Solution Overview
Problem
The manual labeling of entity pairs in knowledge graphs is time-consuming, affecting the training speed and efficiency of knowledge graph alignment models.
Innovation Solution
Select candidate entity pairs based on predicted alignment probabilities and alignment difficulty degrees, reducing the number of pairs that need manual labeling by using a method that includes calculating character similarity, semantic similarity, entity attribute proportion, triplet proportion, and weight, and then training the model with labeled results from selected pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling of all entity pairs is performed, then training data quality is improved, but labeling time and training speed deteriorate
Solution Approach 1:
The system uses the trained model itself to predict alignment probabilities and identify candidate entity pairs that need manual labeling, making the system self-optimizing. The model automatically determines which entity pairs are most valuable to label, reducing reliance on random or exhaustive manual labeling while maintaining training data quality.
Solution Approach 2:
The system changes the parameter of entity pair selection from random or exhaustive to probability-based selection. By using alignment probability thresholds and difficulty degrees as selection criteria, the system transforms the labeling process from a time-intensive exhaustive task to a targeted efficient process that maintains data quality.
2Reliability
If manual labeling of all entity pairs is performed, then model training completeness is improved, but model training speed deteriorates
Solution Approach 1:
The system extracts only the most valuable entity pairs for manual labeling based on alignment probability and difficulty degree, rather than processing all entity pairs. This extraction approach maintains training completeness for critical cases while dramatically reducing the overall labeling workload and accelerating model training speed.
Solution Approach 2:
Instead of performing complete manual labeling of all entity pairs, the system performs partial labeling on the most informative subset. This partial action approach achieves sufficient training completeness for model convergence while improving training speed by avoiding unnecessary labeling of easily alignable or low-value entity pairs.
3Ease of operation
If random selection of entity pairs for labeling is used, then labeling process simplicity is improved, but alignment efficiency deteriorates
Solution Approach 1:
The system incorporates feedback loops where the trained model continuously predicts alignment probabilities, identifies difficult entity pairs, and guides subsequent labeling efforts. This feedback mechanism transforms the simple but inefficient random selection process into an intelligent adaptive process that improves alignment efficiency while maintaining operational simplicity through automated candidate generation.
Solution Approach 2:
The system performs preliminary analysis by calculating alignment probabilities and difficulty degrees before the actual labeling process. This preliminary action identifies the most valuable entity pairs in advance, allowing the labeling process to focus only on high-priority cases and thereby improving alignment efficiency without complicating the labeling workflow.
4Measurement precision
If all entity pairs are labeled, then alignment accuracy is improved, but labeling cost and time increase
Solution Approach 1:
The system applies different quality levels of labeling to different entity pairs based on their alignment probability and difficulty degree. High-probability difficult cases receive manual labeling for high accuracy, while low-probability or easy cases may use automated methods or lower labeling priority. This local quality approach maintains overall alignment accuracy while reducing total labeling time and cost.
Data Source
AI summary
A method for training a knowledge graph alignment model includes selecting first candidate entity pairs from first entity pairs based on a predicted alignment probability of each of the first entity pairs, and calculating an alignment difficulty degree of each of the first candidate entity pairs. The method further includes selecting first target entity pairs from the first candidate entity pairs based on the alignment difficulty degree of each of the first candidate entity pairs, acquiring a labeled alignment result of each of the first target entity pairs, and obtaining a trained knowledge graph alignment model according to the predicted alignment probability and the labeled alignment result of each of the first target entity pairs.