Semantic Labeling System for Automated Data Feature Engineering
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Solution Overview
Problem
Current machine learning tools face challenges in automating feature engineering and data labeling, particularly in handling diverse and noisy data from physical systems, due to the need for domain expertise and lack of standardized meta-descriptors, which hinders efficient data analytics and integration.
Innovation Solution
A method for learning semantic descriptions of data based on physical knowledge using processors, where physical knowledge data and semantic labels are associated with data sources to generate textual descriptors, enabling automated semantic labeling and guiding machine learning model predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning tools are used for data analytics, then computational power and data processing capability are improved, but the need for domain expertise and lack of standardized meta-descriptors increases complexity in automating feature engineering and data labeling
Solution Approach 1:
The patent introduces an intermediary component - a semantic labeling system that uses ontologies and natural language processing - to bridge the gap between raw data and machine learning models. This intermediary automatically generates meta-descriptors and performs semantic labeling, eliminating the need for manual domain expertise while maintaining high data processing capability.
Solution Approach 2:
The system performs preliminary actions by pre-processing data to extract semantic information and generate standardized meta-descriptors before the main machine learning processing. This preliminary semantic analysis automates the feature engineering process, reducing complexity in subsequent data analytics stages.
2Adaptability or versatility
If diverse data from multiple data sources is integrated, then data variety and analytical insights are improved, but the lack of standardized meta-descriptors and heterogeneous data formats increases processing complexity and time
Solution Approach 1:
The patent implements a universal semantic labeling framework that can process diverse data formats from multiple sources through a common ontology-based approach. This multi-functional system automatically adapts to different data types (sensor data, text, images) and generates standardized meta-descriptors, enabling efficient integration of heterogeneous data without increasing processing time.
Solution Approach 2:
The system changes the parameter representation of diverse data by transforming various data formats into a unified semantic parameter space using ontologies. This parameter transformation allows heterogeneous data to be processed uniformly, reducing the time required for data integration while maintaining adaptability to multiple data sources.
3Productivity
If automated semantic labeling is implemented, then data labeling efficiency is improved, but the need for physical knowledge data and semantic ontologies increases system complexity
Solution Approach 1:
The patent implements a self-service semantic labeling system that automatically generates labels by extracting physical knowledge directly from the data and matching it with ontology concepts. The system serves itself by autonomously performing semantic analysis without requiring external manual intervention, thereby improving data labeling efficiency while managing system complexity through automation.
Solution Approach 2:
The system incorporates feedback mechanisms where the semantic labeling results are continuously refined based on the extracted physical knowledge and ontology matching accuracy. This feedback loop improves data labeling efficiency over time while the system learns to manage its own complexity by identifying patterns in the data that reduce the need for complex ontology queries.
Data Source
AI summary
Embodiments for learning semantic description of data based on physical knowledge in a computing environment by a processor. Physical knowledge data and semantic labels associated with data from one or more data sources may be learned. Source attributes of the one or more data sources may be associated with one or more classes and concepts of a plurality of ontologies based on the physical knowledge data and the semantic labels to generate textual descriptors of the data.