Semantic Model Library for Unstructured Text Extraction
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Solution Overview
Problem
Automated content extraction from unstructured documents is challenging due to the lack of inherent structure, making it difficult for computers to identify and extract meaningful data effectively.
Innovation Solution
A system that builds a semantic model library to identify and suggest semantic models matching unstructured text, allowing users to submit input that is then structured and processed using a domain-specific semantic model, enabling content extraction by generating and applying semantic models through machine learning and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated content extraction is performed on unstructured documents using heuristics, then content extraction can be attempted without manual structuring, but extraction accuracy and reliability deteriorate due to lack of inherent structure
Solution Approach 1:
The system performs preliminary structuring of unstructured documents by generating semantic models and extracting entities, relationships, and attributes before the actual content extraction process. This preliminary organization of data into structured formats (JSON, XML, CSV) enables subsequent extraction operations to achieve high accuracy automatically without requiring manual structuring for each extraction task.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms unstructured document content into structured representations through entity recognition, relationship extraction, and attribute identification. This intermediary structured format acts as a bridge between unstructured input and extraction requirements, enabling accurate automated extraction without direct heuristic processing of raw unstructured text.
2Measurement precision
If semantic models are generated through machine learning and user interaction, then structure is added to unstructured text improving extraction accuracy, but system complexity increases
Solution Approach 1:
The system segments the complex task of adding structure to unstructured text into distinct modular components: entity recognition module, relationship extraction module, attribute identification module, and model generation module. Each module handles a specific aspect of structuring, making the overall system more manageable and maintainable while achieving high extraction accuracy through the coordinated operation of these specialized components.
3Productivity
If heuristics are used to extract content from unstructured documents, then some content extraction can be achieved, but reliability and consistency of extraction deteriorate
Solution Approach 1:
The system changes the fundamental parameters of content extraction by transitioning from heuristic-based extraction directly from unstructured text to extraction from structured semantic models. By transforming the input data into standardized formats with defined schemas, data types, and relationships, the extraction process achieves consistent and reliable results across different documents and extraction scenarios.
Data Source
AI summary
Semantic model usage includes receiving, via a graphical user interface, input of unstructured text from a user. While receiving the input, the input is compared to semantic models in a semantic library, and from the semantic model, a semantic model satisfying a threshold is selected. A suggestion is generated from the semantic model, and presented, in the graphical user interface, to the user. An acceptance of the suggestion may be received. The input is augmented with the suggestion and stored.