Analogy Detection Engine for Cognitive Question Answering
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Solution Overview
Problem
Existing cognitive question answering systems face difficulties in identifying and understanding analogies, such as exemplifications, comparisons, metaphors, and parables, due to the complexities of language constructs and human reasoning, which are not effectively captured by word-for-word translations.
Innovation Solution
A system and method that utilize an analogy detection engine to identify and analyze analogies by performing sentence structure and ontological analysis, generating potential meanings from characteristic metadata, and scoring these meanings to determine the best interpretation, leveraging learning methods and ontological mappings to improve the detection and understanding of analogies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated QA systems use word-for-word translation and standard NLP methods, then processing speed is maintained, but accuracy in identifying and understanding analogies deteriorates
Solution Approach 1:
The patent segments the analogy detection process into distinct modules: pattern recognition identifies potential analogical structures, semantic analysis evaluates meaning relationships, and confidence scoring ranks results. This segmentation allows each module to specialize in specific aspects of analogy detection, improving overall accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces intermediary components including a pattern recognition layer that mediates between raw input and semantic analysis, and a confidence scoring mechanism that mediates between multiple potential interpretations. These intermediaries bridge the gap between simple text processing and complex analogy understanding, enabling accurate detection without requiring the entire system to handle all complexity simultaneously.
2Measurement precision
If the system performs comprehensive sentence structure and ontological analysis to detect analogies, then analogy understanding accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary pattern recognition to identify potential analogical structures before conducting full semantic analysis. By pre-processing the input to flag likely analogy candidates using syntactic patterns and lexical cues, the system prepares data in advance, so that when comprehensive ontological analysis is needed, it can focus efficiently on already-identified candidates rather than processing all text uniformly.
Solution Approach 2:
The patent applies partial analysis by performing comprehensive sentence structure and ontological analysis only on sentences identified as potential analogies through preliminary pattern recognition. For non-candidate sentences, the system uses lighter processing. This selective application of intensive analysis maintains high accuracy for analogy detection while reducing overall processing time by avoiding exhaustive analysis of all input text.
3Reliability
If learning methods are applied to match detected patterns to known analogical patterns, then categorization accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent pre-trains the system with known analogical patterns and their categorizations before deployment. During operation, the learned models and extracted features from training are applied directly to new inputs without reprocessing the training data. This preliminary learning phase enables fast, accurate categorization during inference while minimizing ongoing computational overhead.
Solution Approach 2:
The patent uses copying by storing extracted features and patterns from training data in lookup tables and feature databases. During operation, the system copies and compares input features against these pre-stored patterns rather than performing full re-analysis. This allows rapid matching and categorization by leveraging previously computed information, significantly reducing computational resources needed during deployment.
Data Source
AI summary
A system, method, and a computer program product are provided for evaluating an analogical pattern by applying natural language processing to an information source to identify analogical pattern terms in a first analogical pattern, applying deep analysis to refine the analogical pattern terms based on semantic analysis to form metadata for the first analogical pattern, generating interpretations of different combinations of the first analogical pattern terms and the metadata, and then scoring each interpretation for each of the different combinations to select a first interpretation exceeding a predetermined threshold for interpretation of the analogical pattern, thereby evaluating the first analogical pattern.