Knowledge Engine Taxonomy Tagging for NLP Determinism
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Solution Overview
Problem
Cognitive systems in natural language processing are inherently non-deterministic, leading to inconsistent data extraction and incorrect outputs due to the susceptibility of machine learning models to input variations and errors, necessitating the creation of deterministic behavior.
Innovation Solution
A system that employs a knowledge engine to transform ground truth data by constructing and executing a training module to identify and append taxonomy tags, forming a query with synsets and hypernyms, thereby ensuring deterministic data processing and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are used to process natural language in cognitive systems, then the system can learn from data and provide relevant recommendations, but the output becomes non-deterministic and susceptible to input variations and errors
Solution Approach 1:
The patent segments the natural language processing task into multiple deterministic components: entity recognition, relationship extraction, and query generation. Each component operates with defined rules and taxonomies rather than pure machine learning, allowing the system to maintain adaptability through structured approaches while ensuring deterministic output at each processing stage.
Solution Approach 2:
The patent introduces an intermediary layer between the machine learning models and the final output. This intermediary uses predefined taxonomies, ontologies, and validation rules to standardize and verify the output from ML components, transforming non-deterministic ML results into deterministic, reliable outputs that meet system requirements.
2Productivity
If machine learning models process natural language data, then the system can extract entities and provide recommendations, but errors in input documents lead to incorrect data extraction and output
Solution Approach 1:
The patent implements feedback mechanisms where the system validates extracted entities and relationships against predefined taxonomies and business rules. When inconsistencies or errors are detected, the system returns to previous processing stages to correct issues, ensuring high precision in data extraction while maintaining efficient productivity through automated validation loops.
Solution Approach 2:
The patent performs preliminary actions by establishing comprehensive taxonomies, ontologies, and validation rules before processing natural language data. These pre-defined structures guide the machine learning models during extraction, preventing errors from propagating and ensuring high accuracy from the outset while maintaining productive processing speeds.
3Reliability
If taxonomy tags are applied to ground truth data, then data structure and classification improve, but the complexity of data transformation and tag identification increases
Solution Approach 1:
The patent creates universal taxonomies and ontologies that serve multiple functions: classification, validation, query generation, and relationship definition. These multi-functional structures improve data quality across different processing stages without requiring separate complex systems for each function, thereby reducing overall processing complexity while maintaining high reliability.
Data Source
AI summary
A system, computer program product, and method are provided to leverage a taxonomy service to format ground truth data. An artificial intelligence platform processes ground truth data, including identification of one or more applicable taxonomy tags. The identified tags are filtered and applied to the ground truth data, thereby constructing an output string that incorporates the ground truth data together with one or more of the identified tags, effectively transforming the ground truth data. Application of the transformed ground truth data is employed to accurately identify the source and/or meaning of the natural language, and in one embodiment, to product a physical action or transformation of a physical hardware device.