Ontology-Based Document Extraction with Rule Validation
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Solution Overview
Problem
Manual document processing is error-prone, slow, and costly, while existing computer-based information extraction methods are often inaccurate or require deep programming knowledge, making them inaccessible to users without technical expertise.
Innovation Solution
A method and system for extracting information from documents, which involves receiving machine-encoded text, providing a data extracting list and set of rules to validate predefined values, searching for matches, and outputting structured data, allowing users without programming skills to semi-automatically or automatically extract information from documents like safety data sheets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual document processing is used, then accuracy can be maintained through expert review, but processing speed is slow and costs are high
Solution Approach 1:
The patent introduces an intermediary validation layer between automated extraction and final output. A rule-based validation system acts as a mediator that automatically checks extracted data against predefined criteria, constraints, and business rules. This intermediary mechanism enables automated processing while maintaining reliability by filtering and validating results before delivery, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The system implements feedback mechanisms where validation rules provide immediate feedback on extracted data quality. The validation process compares extracted information against predefined constraints and returns validation results that can trigger re-extraction or correction. This feedback loop ensures high accuracy in automated processing without requiring manual review of every extraction, thus improving productivity while maintaining reliability.
2Reliability
If complex information extraction methods are used, then extraction accuracy can be improved, but user accessibility deteriorates due to programming knowledge requirements
Solution Approach 1:
The system enables self-service through automated validation that does not require user intervention or programming knowledge. Predefined validation rules automatically execute against extracted data, providing self-validating extraction capabilities. Users can configure basic parameters through simple interfaces without needing to understand complex extraction algorithms, making advanced extraction capabilities accessible to non-technical users while maintaining high accuracy.
Solution Approach 2:
The patent segments the information extraction system into distinct modular components: extraction module, validation module, and output module. Each module handles specific tasks independently. The validation module contains predefined rules that automatically check extracted data without requiring user programming. This segmentation allows users to benefit from complex validation logic through simple interfaces, improving accessibility while maintaining extraction accuracy.
3Productivity
If automated information extraction is implemented, then processing efficiency increases, but adaptability to new document types decreases due to programming requirements
Solution Approach 1:
The system implements dynamic adaptability through configurable validation rules that can be adjusted without reprogramming. The validation framework allows rules to be modified, added, or removed based on different document types and extraction requirements. This dynamic configuration capability enables the system to adapt to new document formats and extraction needs while maintaining automated processing efficiency, as changes can be made through configuration rather than code modification.
Solution Approach 2:
The patent creates a universal validation framework that can handle multiple document types and extraction scenarios through a single system. The predefined validation rules are designed to be document-type-agnostic, working across various formats and structures. This universality allows the system to maintain high processing efficiency while being adaptable to different document types, as the same validation infrastructure serves multiple purposes without requiring specialized programming for each case.
Data Source
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AI summary
The present invention relates to a method for extracting information from a document, preferably from a safety data sheet. The method is based on a data extracting list and a set of rules, preferably ontology based. Thus, an ontology based information extraction (OBIE) is provided, in particular wherein users (without any programming skills) are able to extract data from the documents and automatically delivering a structured output thereof, e.g. enterprise resource planning.