Neural Link Predictors Quantifying Uncertainty via Dropout Sampling
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Solution Overview
Problem
Traditional machine learning models for predicting missing facts in knowledge graphs lack uncertainty quantification, making it difficult to assess the reliability of predictions, especially in safety-critical scenarios where trustworthiness is crucial.
Innovation Solution
A method that converts target triples in a knowledge graph to embeddings space using neighborhood sampling and repeats the process with dropouts to generate plausibility scores, allowing for the calculation of both predicted plausibility and certainty scores, providing a quantified uncertainty measure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models are used for link prediction in knowledge graphs, then the prediction process is simple and fast, but uncertainty quantification is lacking and reliability is reduced
Solution Approach 1:
The patent applies parameter changes by introducing dropout rates as a controllable parameter. By varying the dropout rate parameter during multiple forward passes, the system generates different plausibility scores that reflect model uncertainty. This allows the same base model to provide both predictions and uncertainty measures without requiring a completely different model architecture.
Solution Approach 2:
The patent implements periodic action by performing multiple forward passes through the neural network with different dropout configurations. Instead of a single prediction, the system repeatedly evaluates the target triple N times with varying dropout patterns, generating a distribution of plausibility scores that quantifies uncertainty while maintaining the same underlying model structure.
2Reliability
If multiple forward passes with dropouts are performed to quantify uncertainty, then certainty scores are provided and reliability is improved, but computational time and complexity increase
Solution Approach 1:
The patent applies partial action by performing a finite number N of forward passes instead of an infinite or exhaustive evaluation. The system determines an optimal stopping point by computing the standard deviation of plausibility scores across the N passes, providing sufficient uncertainty quantification without requiring excessive computational resources. This balances accuracy with efficiency.
3Reliability
If plausibility scores are generated for target triples, then prediction capability is provided, but uncertainty measurement is lacking and trustworthiness is reduced
Solution Approach 1:
The patent implements universality by designing a system that simultaneously performs multiple functions: generating plausibility scores for link prediction, quantifying uncertainty through standard deviation calculation, and providing certified lower bounds on plausibility. All these functions are achieved within a single unified framework using the same neural network model and dropout mechanism, eliminating the need for separate uncertainty estimation modules.
Data Source
AI summary
The present disclosure describes methods and systems for quantifying certainty for a prediction based on a knowledge graph. The method includes receiving a target triple and a knowledge graph comprising a set of structured data and a set of certainty scores for the structured data; converting the target triple to an embeddings space according to neighborhood sampling by a neural network, wherein the embeddings space includes a set of point coordinates; generating a plausibility prediction for the target triple using a scoring function; repeating converting the target triple to the embedding space and generating another plausibility prediction for the target triple N times with dropouts to obtain N plausibility scores for the target triple, wherein N is an integer larger than one; generating a predicted plausibility score and a certainty score for the target triple; and outputting the predicted plausibility score and the certainty score.