Robust Causal Graph Representation via Similarity Thresholds
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Solution Overview
Problem
Existing natural language processing methods for graph representation of causal relationships in documents are sensitive to input conditions and processing strategies, leading to inconsistent graph representations when the input documents remain the same, which undermines their reliability for downstream tasks.
Innovation Solution
A method that uses a first language model to extract cause-effect pairs from natural language documents, constructs a graph representing these pairs, measures similarity with another graph constructed under different conditions, and incorporates the first graph into a knowledge base if the similarity exceeds an acceptance threshold, ensuring a robust representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different input conditions or processing strategies are used to generate graph representations, then the graph generation process becomes more flexible and adaptable, but the consistency and reliability of the graph representation deteriorates
Solution Approach 1:
The patent segments the graph representation into multiple candidate graphs generated from different input conditions and processing strategies. Each candidate graph is evaluated independently, and the final representation is formed by aggregating or selecting from these segmented candidates, thereby maintaining consistency across variations.
Solution Approach 2:
The patent implements a feedback mechanism where the system evaluates the quality and consistency of generated graphs by comparing multiple candidate graphs. Based on this evaluation feedback, the system iteratively refines the graph representation to achieve greater consistency while preserving adaptability to different input conditions.
2Reliability
If multiple graph representations are generated and compared to ensure robustness, then the reliability of the graph representation improves, but the computational complexity and processing time increases
Solution Approach 1:
The patent applies partial action by generating and comparing only a subset of candidate graphs rather than exhaustively evaluating all possible variations. This selective approach maintains robustness by comparing sufficient diverse candidates while avoiding the computational burden of exhaustive generation, thus reducing overall complexity.
3Measurement precision
If graph representations are made robust through multiple comparisons and validations, then the accuracy of downstream tasks improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-generating and pre-comparing multiple candidate graphs before the actual downstream tasks are executed. This advance preparation ensures that the final graph representation is already validated for robustness, thereby improving downstream task accuracy without adding significant processing time during task execution.
Data Source
AI summary
An embodiment extracts, from a natural language document, using a first language model, a set of cause-effect pairs, each cause-effect pair comprising a pair of phrases, each phrase comprising a portion of the natural language document. An embodiment constructs a first graph representing the set of cause-effect pairs, each node in the first graph representing a phrase in the set of phrases, each edge in the first graph representing a cause-effect relationship between nodes connected by an edge. An embodiment measures a graph similarity between the first graph and a second graph constructed from phrases extracted from the natural language document. An embodiment incorporates, into a knowledge base responsive to determining that the graph similarity is above an acceptance threshold, the first graph.