Facts Extraction System for Unstructured Documents
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Solution Overview
Problem
Current information retrieval systems face challenges in transforming unstructured and semi-structured natural language text into a structured format for efficient analysis and storage, particularly due to the complexity of web pages with dynamic content and the lack of standardized formats, leading to high false positive rates and scalability issues.
Innovation Solution
A system that utilizes a multi-parallel architecture and hybrid knowledge agents to crawl and analyze the web, extract relevant information, and convert it into a structured format, employing crystallization points for efficient crawling and iterative verification to reduce false positives, and incorporating island grammar for parsing and timestamp extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If keyword search is used to find information on the web, then the system can handle unstructured data, but the false positive rate increases to 10-20%
Solution Approach 1:
The patent segments the information extraction process into multiple independent modules: document acquisition, fact extraction, verification, and storage. Each module handles specific tasks with dedicated algorithms, allowing the system to process unstructured data while maintaining reliability through modular verification steps.
Solution Approach 2:
The patent implements feedback mechanisms where extracted facts are verified against multiple sources and cross-referenced with existing knowledge bases. The verification module provides feedback to adjust extraction parameters and reduce false positives while maintaining adaptability to new data formats.
2Productivity
If statistical ontology generation is used to navigate information, then the system can process large volumes of data, but it cannot aggregate information into structured formats
Solution Approach 1:
The patent introduces an intermediary structured knowledge base that sits between unstructured web data and query systems. Fact extraction algorithms convert unstructured text into standardized fact representations with defined schemas, enabling both high-volume processing and precise structured storage for reliable information aggregation.
3Adaptability or versatility
If web crawling is performed without standardized formats, then the system can access diverse content, but the complexity of analysis increases
Solution Approach 1:
The patent changes the parameter representation of web content from diverse unstructured formats to standardized fact schemas. By transforming various document types (HTML, XML, plain text) into uniform fact structures with consistent attributes and relationships, the system maintains broad content accessibility while reducing analysis complexity through parameter standardization.
Data Source
AI summary
Provided are methods and systems that extract facts of unstructured documents and build an oracle for various domains. The present invention addresses the problem of efficient finding and extraction of facts about a particular subject domain from semi-structured and unstructured documents, makes inferences of new facts from the extracted facts and the ways of verification of the facts, thus becoming a source of knowledge about the domain to be effectively queried. The methods and systems can also extract temporal information from unstructured and semi-structured documents, and can find and extract dynamically generated documents from Deep or Dynamic Web.