Explanatory Paths for Column Annotations via Knowledge Graph
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Solution Overview
Problem
Conventional data analytics systems face challenges in accuracy, flexibility, and efficiency due to the black box nature of complex prediction models, leading to inadequate transparency and incorrect label matches, which result in inefficiencies and functional performance issues.
Innovation Solution
The system employs a knowledge graph and deep representation learning to generate explanatory paths for column annotations, providing transparent and accurate label determination by encoding columns, features, and candidate labels, and using cumulative distance scores and diversified ranking to select diverse explanatory paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex prediction models such as neural networks are used to generate label matches, then matching performance is improved, but transparency of the model deteriorates
Solution Approach 1:
The patent introduces an explanation generation module as an intermediary between the complex neural network model and the user. This module takes the internal representations and activation patterns from the neural network and transforms them into human-interpretable explanation paths using knowledge graphs, thereby mediating between high-performance black-box modeling and transparent interpretation.
Solution Approach 2:
The patent segments the explanation generation process into distinct components: (1) extracting relevant features and activation patterns from the neural network, (2) mapping these to entities in a knowledge graph, (3) generating explanation paths through the knowledge graph, and (4) presenting these paths to users. This segmentation allows each component to be optimized independently while maintaining overall transparency.
2Loss of information
If less complex prediction models such as rule-based approaches are used, then transparency is improved, but matching performance deteriorates
Solution Approach 1:
The patent merges two previously separate approaches into a unified system: complex neural network models for high-accuracy label matching and knowledge graph-based explanation systems for transparency. The neural network handles the complex pattern recognition and prediction, while the knowledge graph handles the interpretability and explanation generation, allowing both strengths to coexist.
3Measurement precision
If conventional systems require user interaction to verify matched labels, then accuracy can be improved, but efficiency deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate and present explanation paths for matched labels without requiring user intervention. The explanation generation module autonomously creates interpretable paths that allow users to verify accuracy independently, eliminating the need for manual verification while maintaining system efficiency.
4Productivity
If conventional systems do not provide explanations for label matches, then efficiency is improved, but reliability deteriorates due to undetected incorrect matches
Solution Approach 1:
The patent implements feedback by generating explanation paths that provide users with rationale for each label match. These explanations serve as feedback mechanisms that allow users to understand the reasoning behind matches, verify correctness, and identify potential errors without requiring additional verification steps, thus maintaining both efficiency and reliability.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media are disclosed for generating generate explanatory paths for column annotations determined using a knowledge graph and a deep representation learning model. For instance, the disclosed systems can utilize a knowledge graph to generate an explanatory path for a column label determination from a deep representation learning model. For example, the disclosed systems can identify a column and determine a label for the column using a knowledge graph (e.g., a representation of a knowledge graph) that includes encodings of columns, column features, relational edges, and candidate labels. Then, the disclosed systems can determine a set of candidate paths between the column and the determined label for the column within the knowledge graph. Moreover, the disclosed systems can generate an explanatory path by ranking and selecting paths from the set of candidate paths using a greedy ranking and/or diversified ranking approach.