Structured Knowledge Base for Neural Network Interpretability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural networks, particularly foundation models, face challenges in accurately modeling knowledge due to latent representation of knowledge in model parameters, leading to issues with verification, correction, and the need for retraining with large data and computational costs, often producing plausible but incorrect outputs.
Innovation Solution
A structured knowledge base is introduced, allowing neural networks to read from and write to a knowledge model using a novel attention mechanism, enabling gradient-based training and updates while maintaining interpretability, verifiability, and editability, represented as property name-value pairs with embeddings for tensor operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If knowledge is encoded in a structured knowledge base with property name-value pairs and embeddings, then interpretability and verifiability of knowledge are improved, but the complexity of the system architecture increases due to the need for attention mechanisms and tensor operations
Solution Approach 1:
The patent introduces a structured knowledge base as an intermediary component between neural networks and external knowledge sources. This knowledge base uses property name-value pairs with embeddings that serve as a mediator, enabling interpretability and verifiability while managing complexity through standardized tensor operations and attention mechanisms.
2Adaptability or versatility
If a novel attention mechanism is used to read from and write to the knowledge model, then the ability to query and update structured knowledge is improved, but the computational cost and training time increase
Solution Approach 1:
The patent performs preliminary action by pre-processing and embedding knowledge into the structured knowledge base before neural network training. The property name-value pairs are converted into embeddings in advance, allowing the attention mechanism to efficiently query and update knowledge during training without performing complex computations in real-time, thus reducing training time while maintaining versatility.
3Productivity
If gradient-based training is applied to update the knowledge base and neural networks, then the speed and scale of knowledge learning are improved, but the risk of producing plausible but incorrect outputs increases
Solution Approach 1:
The patent implements feedback mechanisms where the structured knowledge base with property name-value pairs provides verifiable constraints and ground truth information that feedback into the gradient-based training process. This feedback loop allows rapid knowledge learning while maintaining accuracy by using the structured knowledge as a reference to correct plausible but incorrect outputs generated during training.
Data Source
AI summary
An approach to structured knowledge modeling and the incorporation of learned knowledge in neural networks is disclosed. Knowledge is encoded in a knowledge base (KB) in a manner that is explicit and structured, such that it is human-interpretable, verifiable, and editable. Another neural network is able to read from and/or write to the knowledge model based on structured queries. The knowledge model has an interpretable property name-value structure, represented using property name embedding vectors and property value embedding vectors, such that an interpretable, structured query on the knowledge base may be formulated by a neural model in terms of tensor operations. The knowledge base admits gradient-based training or updates (of the knowledge base itself and/or a neural network(s) supported by the knowledge base), allowing knowledge or knowledge representations to be inferred from a training set using machine learning training methods.