Semantic Processor for Cause-Effect Knowledge Extraction
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Solution Overview
Problem
Current knowledge extraction methods from natural language documents are limited to shallow analysis and structured databases, failing to effectively recognize cause-effect relations between facts, which are crucial for understanding outside world regularities.
Innovation Solution
A system and method that uses expanded Subject—Action—Object (eSAO) semantic units with additional components like Adjective, Preposition, and Adverbial to perform deep linguistic and semantic analysis, recognizing cause-effect relations through a Semantic Processor that generates a Cause-Effect Knowledge Base by analyzing text and applying patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If shallow linguistic analysis is performed on structured databases, then processing speed is improved, but knowledge extraction depth deteriorates
Solution Approach 1:
The patent segments the knowledge extraction process into multiple stages: initial shallow analysis for rapid processing, followed by deep semantic analysis for cause-effect relation extraction. This allows the system to maintain high processing speed for basic operations while achieving deep knowledge extraction when needed, resolving the contradiction between productivity and measurement precision.
Solution Approach 2:
The patent introduces a new dimension of analysis by extracting cause-effect relations between facts, going beyond traditional subject-action-object extraction. This dimensional extension enables the system to capture temporal and causal relationships, thereby increasing knowledge extraction depth without sacrificing the efficiency of basic processing operations.
2Measurement precision
If deep linguistic analysis is performed on arbitrary text documents, then knowledge extraction accuracy is improved, but processing time deteriorates
Solution Approach 1:
The patent performs preliminary actions by first identifying and extracting basic facts and their relationships before conducting deep cause-effect analysis. This preliminary structuring of information enables subsequent deep analysis to operate on pre-processed data, improving accuracy while reducing the time required for complex analysis compared to performing deep analysis on raw text.
Solution Approach 2:
The patent extracts and isolates cause-effect relations as a separate layer of knowledge representation from the underlying facts. By taking out the causal relationships and representing them separately, the system can perform accurate deep analysis on cause-effect pairs without re-processing all raw text, thereby improving accuracy while controlling processing time.
3Ease of operation
If traditional fact extraction is used, then ease of operation is improved, but ability to recognize cause-effect relations deteriorates
Solution Approach 1:
The patent creates a universal knowledge representation framework that handles both traditional fact extraction and cause-effect relation recognition using the same eSAO structure. This multi-functional approach allows the system to maintain ease of operation for basic fact extraction while simultaneously providing advanced cause-effect analysis capability, eliminating the need to choose between simplicity and versatility.
Solution Approach 2:
The patent nests cause-effect relations within the existing fact extraction framework by representing causal relationships as specialized eSAO structures. This nesting approach allows traditional fact extraction operations to remain simple and easy to operate, while the nested cause-effect layer provides enhanced analytical capability without complicating the basic extraction process.
4Manufacturing precision
If structured database approach is used, then manufacturing precision is improved, but adaptability to arbitrary text documents deteriorates
Solution Approach 1:
The patent changes the structural parameters of knowledge representation by introducing temporal components and cause-effect relationship fields into the eSAO structure. This parameter extension allows the system to maintain consistent structured representation (manufacturing precision) while adapting to the diversity of arbitrary text documents through flexible temporal and causal relationship modeling, resolving the contradiction between structure consistency and adaptability.
Data Source
AI summary
A Semantic Processor for the recognition of Cause-Effect relations in natural language documents which includes a Text Preformatter, a Linguistic Analyzer and a Cause-Effect Knowledge Base Generator. The Semantic Processor provides automatic recognition of cause-effect relation both inside single fact and between the facts in arbitrary text documents, where the facts are also automatically extracted from the text in the form of seven-field semantic units. The recognition of Cause-Effect relations is carried out on the basis of linguistic (including semantic) text analysis and a number of recognizing linguistic models built in the form of patterns.