Knowledge Graph Construction via Neural Triple Prediction

The system implements feedback through probability calculations and cross-entropy measurements that continuously evaluate the quality of predicted triples. The cross-entropy loss function provides feedback on prediction accuracy, allowing the model to iteratively improve its entity and relationship determination while maintaining high productivity through automated processing.

The patent replaces manual knowledge graph population methods with an automated neural network model that processes text data. This substitution of mechanical/manual operations with an intelligent system enables both high productivity through automation and maintained precision through learned patterns from training data.

The system performs self-service by automatically extracting entities and relationships from text data without requiring manual intervention. The neural network model independently processes input text, generates predictions, calculates probabilities, and populates the knowledge graph autonomously, achieving both high productivity and acceptable precision through self-directed operation.

Manual knowledge graph creation processes are replaced with an automated neural network system that uses machine learning to determine entities and relationships. This substitution enables rapid processing of large text volumes while maintaining precision through the model's learned understanding of linguistic patterns and semantic relationships.

The system optimizes the balance between model complexity and precision by adjusting parameters such as the number of neural network layers, embedding dimensions, and probability thresholds. The cross-entropy loss function and gradient descent optimization enable precise parameter tuning that achieves high classification accuracy without unnecessarily increasing model complexity.

The patent applies partial action by using a neural network model of appropriate complexity rather than the maximum possible complexity. The system uses just enough model capacity to achieve the required precision for triple classification, avoiding the diminishing returns and increased complexity that would result from overly sophisticated models.

The system manages model complexity through parameter optimization using gradient descent and cross-entropy loss minimization. By carefully tuning parameters such as network depth, width, and regularization strength, the patent achieves high measurement precision for triple classification while controlling device complexity to practical levels.

The patent employs a neural network model with complexity matched to the requirements of the task. Rather than using the most complex model available, the system applies partial action by selecting a model architecture and parameter set that provides sufficient precision for knowledge graph population without unnecessary complexity, achieving an optimal balance between accuracy and computational resources.