Document Information Extraction via Knowledge Graph Fusion
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Solution Overview
Problem
Traditional methods for extracting structured information from digital documents are labor-intensive, prone to errors, and limited in scope, especially when dealing with varied document layouts and multi-hop extraction scenarios, as they rely on fixed templates and high annotation costs.
Innovation Solution
A method that uses knowledge graphs and prompt-based learning to extract information from documents by performing OCR, encoding text and position vectors, building a knowledge graph, and generating fusion vectors to identify key-value pairs without requiring traditional template constructions, enabling automatic layout structure identification and improved accuracy in zero-sample prediction scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional template-based methods are used for information extraction, then the extraction process is simple to implement, but the accuracy and adaptability to varied document layouts deteriorate
Solution Approach 1:
The patent replaces traditional template-based mechanical matching systems with a deep learning-based semantic understanding system. The system uses pre-trained language models to automatically comprehend document content and structure, eliminating the need for manual template construction while significantly improving extraction accuracy across varied document layouts.
Solution Approach 2:
The patent transforms the extraction approach by changing from fixed template parameters to dynamic semantic representations. By using vector embeddings and attention mechanisms, the system adapts to different document structures without requiring template modifications, thereby improving both accuracy and adaptability.
2Ease of manufacture
If fixed templates are used for information extraction, then the development process is straightforward, but the system cannot handle multi-hop extraction scenarios and varied document layouts
Solution Approach 1:
The patent introduces dynamic adaptability by using pre-trained language models that can automatically adjust to different document layouts and extraction scenarios. The system dynamically identifies document structures and relationships without requiring pre-defined templates, enabling it to handle varied layouts and multi-hop extraction tasks effectively.
Solution Approach 2:
The patent creates a universal extraction system that can handle multiple document types and extraction scenarios through a single pre-trained model. The model's semantic understanding capabilities allow it to perform various extraction tasks across different document formats without requiring separate templates or models for each scenario.
3Ease of manufacture
If traditional OCR and template methods are used, then the system is easy to deploy, but extensive annotation and template development are required
Solution Approach 1:
The patent applies preliminary action by using pre-trained language models that have already learned document structures and semantics during their training phase. This pre-training eliminates the need for extensive annotation and template development when deploying the system, as the model can directly apply its learned knowledge to new documents with minimal additional preparation.
4Reliability
If manual template construction is used, then the extraction logic is explicit and controllable, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The patent replaces manual template construction with an automated deep learning system that maintains extraction control through attention mechanisms and interpretability features. The system automatically learns extraction patterns from data while providing explainable results, thereby improving productivity without sacrificing control or reliability.
Data Source
AI summary
An embodiment for a method of extracting information from documents using knowledge graphs and prompt-based learning. The embodiment may receive a document and perform optical character recognition (OCR) to obtain OCR text lines and associated bounding boxes. The embodiment may encode each of the obtained OCR text lines into semantic vectors and each of the associated bounding boxes into position vectors to generate a knowledge graph using fusion vectors derived therefrom. The embodiment may receive a query including a key value. The embodiment may identify a series of candidate nodes including a series of most similar nearby nodes positioned near a first node associated with the key value. The embodiment may generate prompt template to determine closeness of the candidate nodes to the key value and calculate associated confidence levels. The embodiment may output extraction information associated with the candidate node having a highest calculated confidence level.