Automated Entity Relationship Model Generation via Context-Aware Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for generating Entity Relationship (ER) models in service and support industries rely heavily on human resources and suffer from human errors, lack of intelligence, and inefficiencies, failing to leverage existing knowledge effectively.
Innovation Solution
A system and method for automatic ER model generation using a processor with a training module, validation module, and knowledge generation module, which trains an information extraction model from documents and existing ER models, extracts features, and generates ER models using a context-aware system, thereby automating knowledge acquisition and reducing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human resources are used for knowledge acquisition and ER model generation, then the process can be performed with existing tools and interfaces, but human errors increase and productivity decreases
Solution Approach 1:
The system enables self-service through automated information extraction and ER model generation. The information extraction model automatically processes documentation and existing ER models to generate new ER models without human intervention, eliminating human errors while maintaining high productivity through automated knowledge acquisition and model generation processes
Solution Approach 2:
The patent replaces the mechanical human-operated process with an automated system. The information extraction model and validation module substitute human analysts, using machine learning and automated validation to improve both accuracy and efficiency. The system processes documentation and existing models through automated algorithms rather than manual human analysis
2Productivity
If automated information extraction is implemented, then productivity increases and human errors are reduced, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: information extraction model, validation module, and ER model generation module. Each module performs a specific function - extracting information from documentation, validating the extracted information, and generating the final ER model. This segmentation manages complexity by creating independent, manageable components that can be developed and maintained separately
Solution Approach 2:
The validation module serves as an intermediary between information extraction and ER model generation. It acts as a mediator that verifies the quality and accuracy of extracted information before it is used to generate ER models, ensuring reliability while managing system complexity through a dedicated validation layer
3Measurement precision
If existing knowledge is leveraged in ER model generation, then the intelligence of the system improves and consistency increases, but the processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and validating information from existing ER models during the training phase. The information extraction model is trained on existing ER models and documentation beforehand, so that when generating new ER models, it can leverage this pre-processed knowledge efficiently. This preliminary preparation improves consistency while minimizing processing time during actual model generation
Solution Approach 2:
The validation module provides feedback mechanisms that ensure consistent application of extracted knowledge. By validating extracted information against established patterns and existing models, the system maintains measurement precision and consistency. The feedback loop ensures that learned knowledge is consistently applied without requiring excessive processing time
Data Source
AI summary
System and method for automatic entity relationship (ER) model generation for services as software is disclosed. ER model generation by automated knowledge acquisition is disclosed, and automation of knowledge generation process is disclosed. Information extraction process is automated and multilevel validation of information extraction process is provided. System comprises training module to train information extraction model, and knowledge generation module for population of ER model. Standard Operators are generated based on the ER model so generated (populated). Context aware entity extraction is implemented for the ER model generation. System and method leverages existing ER model to make the system self-learning and intelligent, and provides common platform for knowledge generation from different data sources comprising documents, database, website, web corpus, and blog.