Knowledge Graph Hypothesis Generation With Explainable Rule Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Knowledge graph embedding (KGE) models are criticized for their opaque, black-box nature, making it difficult to draw clear, interpretable connections between input features and model outputs, posing challenges for explainability.
Innovation Solution
A novel post-hoc explainable AI method for KGE models that decodes latent representations into human-understandable symbolic rules and facts by identifying statistical regularities in subgraph neighborhoods, providing immediate and localized explanations without retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If knowledge graph embedding models use high-dimensional latent representations to capture complex relationships, then the model's predictive accuracy is improved, but the explainability and interpretability of the model deteriorates
Solution Approach 1:
The patent introduces an intermediary explanation layer that mediates between the complex KGE model and the user. This layer includes components such as the explanation generator, subgraph extractor, and rule miner that translate opaque latent representations into interpretable explanations without altering the original model's predictive accuracy.
Solution Approach 2:
The patent extracts specific subgraphs and latent representations from the complex KGE model that are most relevant to making predictions. By taking out and analyzing these specific components separately, the system can explain the model's reasoning without needing to simplify the entire complex model.
2Loss of information
If traditional explanation methods attribute parts of input as relevant to model output, then some level of explainability is achieved, but the explanations fail to capture the underlying mechanisms and rationales of KGE models
Solution Approach 1:
The patent applies partial action by focusing explanation efforts on specific subgraphs and latent representations that are most critical to the prediction, rather than attempting to explain all aspects of the complex model. This selective approach provides faithful explanations of the key reasoning mechanisms without the computational burden of comprehensive explanation.
Solution Approach 2:
The patent segments the explanation task into multiple components: subgraph extraction, latent representation analysis, rule mining, and explanation generation. This segmentation allows each component to be optimized for its specific function and enables the system to capture underlying mechanisms more effectively than holistic explanation methods.
3Loss of information
If post-hoc explanation methods are applied to KGE models, then explainability is improved, but the computational overhead and complexity of the explanation system increases
Solution Approach 1:
The patent designs a universal explanation framework that can handle multiple types of KGE models and prediction scenarios through a single integrated system. The explanation generator, subgraph extractor, and rule miner are designed to work across different model architectures and prediction tasks, reducing the need for model-specific explanation code and simplifying the overall system.
4Loss of information
If detailed explanations are generated for each prediction, then user understanding is improved, but the time and computational resources required for real-time application increase
Solution Approach 1:
The patent performs preliminary actions by pre-extracting and caching subgraphs and latent representations that are likely to be needed for explanations. This preprocessing allows the explanation generation step to operate more quickly when predictions are made, as much of the heavy lifting has already been done in advance.
Solution Approach 2:
The patent provides partial explanations by focusing on the most critical subgraphs and latent representations that drive the prediction, rather than generating exhaustive detailed explanations for all aspects of the model. This selective approach maintains user understanding while reducing computation time.
Data Source
AI summary
A method, apparatus, and non-transitory computer-readable medium for generating a hypothesis from a knowledge graph. The method comprises processing the knowledge graph comprising a plurality of fact triples. Each fact triple of the plurality of fact triples comprises two concepts of a set of concepts and one relationship of a set of relationships. Each fact triple is also associated with at least one source. The method further comprises generating the hypothesis from data representing multiple triples, the hypothesis. The hypothesis includes at least one predicted triple having a concept-concept relationship not found in the knowledge graph. The method then outputs at least one source and/or explanation data for the hypothesis.